CONDITION MONITORING AND DIAGNOSTIC ENGINEERING MANAGEMENT (COMADEM 2001)
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CONDITION MONITORING AND DIAGNOSTIC ENGINEERING MANAGEMENT (COMADEM 2001) Proceedings of the 14'" international Congress 4 - 6 September 2001, Manchester, UK
Edited by
Andrew C Starr university of Manchester, UK
RajBKNRao COMADEM international
2001 ELSEVIER Amsterdam • London • New York • oxford • Paris • shannon • Tol
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removes many discrete frequency components, but hardly reduces that at shaft speed, presumably because in this band the modulation was dominated by the bearing fault. Cyclostationary Analysis In a new study (Randall & Antoni, 200IB), an alternative approach is suggested, which also should distinguish the modulation associated with a bearing fauh from that associated with a gear fault, even if they are at the same frequency, as in the above case. It is based on the properties of cyclostationary processes, which lie somewhere between periodic and stationary random signals. A general nonstationary signal can be characterised by its autocorrelation function, defined by: R^{t,x) = E[x{t-xl2)x{t
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With cyclostationary signals, the Fourier transform with respect to t gives discrete frequency components (cyclic frequency), even if that with respect to x gives narrrow-band or broad-band random spectra (vs normal frequency). Bearing signals are approximately cyclostationary, but differ in two respects (Randall & Antoni, 200IB). Firstly, because the fault frequencies tend to be modulated by non-commensurate frequencies (eg inner race frequency by shaft speed), they are not periodic, but rather quasi-periodic. Secondly, because they do not have a defined mean period, their autocorrelation function is not truly periodic with t, which leads to a smearing of higher harmonics, as seen in the envelope spectrum of Fig. 1(f). However, with limited slip of about 1%, which is typical, they are close to periodic, and the envelope spectra are almost discrete frequency as in Fig. 1(f). Figure 6(a) shows the spectral correlation of a signal from a bearing with an inner race fauh. It is seen to be spread out in the normal frequency direction, but effectively discrete in the cyclic frequency direction. In Ref (Randall et al, 2001 A), it is shown that the integral of the spectral correlation over all frequency is equal to the Fourier transform of the mean squared signal, effectively the spectrum of the squared envelope. Figure 6(b) represents both, and can be seen to be that of a typical inner race fault (cf Fig. 3(a)). At first glance there does not appear to be any advantage in the spread of the spectral correlation over normal fi-equency, but it can in fact be used to distinguish between periodic and cyclostationary signals. A periodic signal has discrete components in both frequency directions, whereas cyclostationary signals are distributed in t h e / direction. Since stationary random signals do not change with t, they have a component only at zero cyclic frequency (the normal power spectrum). Thus, if the spectral correlation is evaluated for a cyclic frequency coinciding with one of the discrete components other than zero, it will not be contaminated by additive noise, but on the other hand will be continuous for cyclostationary signals, and localised for periodic signals. Figure 7 shows how this can be applied to the same signals as in Fig. 5. It compares signals from a Seahawk helicopter with and without the inner race bearing fault, evaluated for a = zero and the shaft speed. For the former, there is little difference because of masking by stationary noise, while with the latter the increase and smoothing at many frequencies, due to the fault, points to the presence of a cyclostationary component, presumably a bearing fault (a gear fault would repeat periodically at the shaft speed).
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Cyclic frequency (Hz) Figure 6 (a) Spectral correlation and (b) integrated spectral correlation for the demodulated signal of an inner race fault in the spectral band [2800 Hz; 3300 Hz]. BPFI = 120 Hz. Shaft speed = 9.5 Hz.
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Figure 7 Spectral correlation at particular values of cyclic frequency a with and without bearing fault (a) a = 0 (b) a = shaft speed Q
Planet Bearing Faults A technique has also been developed to assist with the effects of the varying signal transmission path from a faulty planet bearing to an externally mounted transducer. The latter gives some weighting to the signal which without any correction gives an artificial reduction in effective length of envelope signal analysed (the signal being strongest when the faulty bearing is closest to the transducer). Experiments carried out on a planet bearing rig (without gears) showed that making a logarithmic conversion of the envelope signal changed the multiplicative weighting effect to an additive one, thus effectively extending the length of valid signal and improving the resolution of the resulting spectrum.
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RESULTS To demonstrate the performance of the signal processing chain, three partial rub signatures are added to AE noise from a low pressure bearing of a steam turbine unit operating at full load so as to be completely masked by inspection in the time domain, as shown in figure-4a. These were injected with a period of 0.02 sees to simulate once-perrev partial rubbing upon a real turbine. For this relatively high noise scenario, the SNR was calculated in terms of power as 0.5dB. However, after applying the AR matched filter, derived dkectly from a number of candidate signatures from the test rig, a visible SNR gain or recognition differential is introduced corresponding to 5.6dB, as shown in figure-4b. To examine the filter performance over a range of input SNR scenarios, further input SNRs were applied and die filter gain plotted. Figure-5a depicts the AR filter performance in terms of filter gain against the range of input SNR scenarios. As indicated, the recognition differential increases with respect to the SNR of the input, although this reaches a maximum level at -'16.2dB. It is noted that the scenario depicted in figure-4 corresponds to a relatively low gain upon this curve. Intuitively, this relationship between input SNR scenario and filter gain follows as the larger the signal component is within the input, the better the correlation with the fine-scale shape of the replica template. To compare the performance of the AR filter with that of bandpass filtering alone, different combinations of bandpass filters were applied to the AE data. Figure-5b shows one of the better results achieved for a standard FIR filter structure. As indicated, the performance is well below that of the AR matched filtering technique.
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26
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Figure-5: Recognition differential for (a) the AR matched filter (b) a bandpass FIR filter To illustrate the wavelet-based smoothing, once-per-rev partial rub signatures were again buried in turbine noise so as to be completely masked in the time domain. This low SNR scenario was then AR filtered and integrated as depicted in figure-6a. As evident by this AE response, the three rub signatures are now clearly visible. However, other transient *noise* features are also present, and these effectively increase the probability of the diagnostician effecting a false alarm. Therefore, the wavelet filter was applied to smoothout such transients. Essentially, the DWT filter involved transforming tihe time-domain response shown in figure-6a into corresponding wavelet coefficients, using the Daubechie-20 basis function. Then, only wavelet coefficients that exceeded the magnitude of the wavelet domain standard deviation were inverse transformed back into the time domain, with all coefficients below this threshold set to zero. The effect that this hard threshold DWT filtering technique had upon the AE response is shown in figure6.b. Clearly, the filter manages to remove the unwanted transient spikes completely and produce a smooth noise response. Moreover, it is important to note that this filter did not significantly reduce the magnitude of the genuine rub signatures, and therefore does not significantly diminish the probability of rub detection. In contrast, other smoothing filters were unable to smooth out the unwanted transient features without significantly reducing the amplitude of real rub signatures. uInsmoothedfllt«routput
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27
CONCLUSIONS Clearly, scope exists for AE-based detection of partial shaft-seal rubbing within largescale turbine units. This paper demonstrates that, assuming that turbine noise can be approximated as additive, a relatively simple signal processing scheme can be employed to introduce an adequate SNR gain or recognition differential. In particular, the correlation filtering technique in which Autoregressive model coefficients are used to directly derive non-recursive filter coefficients proved effective. The success of this AR filtering technique implies that there is a fundamental difference between the fme-scale shape of rub signal-plus-noise and noise scenarios. Moreover, it is shown that hard threshold discrete wavelet filtering can be useful for smoothing the output AE energy response, so as to remove spurious signal features without too severe reduction in the rub signal amplitude. From this stage, addition signal processing gain may be possible for periodic once-perrev partial rub features via integration across rotation periods or appropriate timefrequency display. Clearly, the detection performance of the signal processing scheme can be quantified more formally by investigating noise and signal-plus-noise amplitude statistics and an appropriate detection criterion established (i.e. Neyman-Pearson). In parallel with rub detection at an acoustic display, a data processing scheme for automatic rub detection and more detailed rub diagnosis has been developed.
REFERENCES Board (2000), Stress wave analysis of turbine engine faults. Aerospace Conference, IEEE Proceedings (Cat. No.00TH8484), voi.6, pp79-93 Donoho (1995), Denoising by soft thresholding, IEEE transactions on Information Theory, vol 41, pp613 Haykin S (1984), An Introduction to Adaptive Filters, Macmillan Publishing Mba D Bannister R H (1999), Condition monitoring of low-speed rotating machinery using stress waves: Parti and Part 2, Proc Instn Mech Engrs, Vol 213, Part E, ppl53 Melton R (1982), Classification of NDE waveforms with Autoregressive models, Journal of Acoustic Emissions, voU, no4, pp266 Muszynska (1989), Rotor-Stationary element rub-related vibration phenomena in rotating machinery-Literature Survey, The Shock and Vibration Digest, vol21, no3, pp3-l 1 Pollard (1977), Sound Waves in Solids- Applied Physics Series, Pion Limited Sato I (1990), Rotating machinery diagnosis with acoustic emission techniques, Electrical EngngJapan, 110(2), 115-127 Tandon and Choudhury (1999), A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribology International, vol32, pp469 Turin, An Introduction to Matched filters, IRE Trans, Inform. Theory, vol6, 1960, pp311 West Venkatesan (1996), Detection and Modeling of Acoustic Emissions for Fault diagnostics, IEEE, 0-8186-7576-4/96
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
CONDITION MONITORING OF VERY SLOWLY ROTATING MACHINERY USING AE TECHNIQUES Dr. Trevor J. Holroyd Holroyd Instruments Ltd Via Gellia Mills, Bonsall, MATLOCK, DE4 2AJ, UK tel: +44 (0)1629 822060, email:
[email protected] ABSTRACT In industry it is often very slowly rotating machinery which is the most critical to the production process as well as being the largest and the highest value. These factors combine to increase the economic requirement for Condition Based Maintenance and hence the importance of suitable means of Condition Monitoring. However slow rotational speeds result in reduced energy loss rates from damage related processes and because of this Condition Monitoring techniques which detect energy loss tend to be more difficult if not impossible to apply. Perhaps surprisingly this is not the case for the Acoustic Emission (AE) technique which is well suited to detecting very small energy release rates. As a result AE is able to detect subtle defect related activity from machinery, even when it is rotating very slowly. In this paper a new AE based signal processing approach is introduced which can provide simple but sensitive means of detecting the presence and evolution of faults in very slowly rotating machinery. These developments have frirther led to the creation of what is believed to be the first easily retro-fitted and affordable on-line monitoring module for very slowly rotating machinery.
KEYWORDS AE, Acoustic Emission, Bearings, CM, Condition Monitoring, Rotating Machinery, Slow Speed.
INTRODUCTION It is well known that the application of traditional Vibration monitoring techniques becomes progressively more difficult to apply as the speed of rotation of a machine decreases. The reasons for this are fourfold : a) the energy release ratesfromdefects reduces as speed reduces.
29
b) the associated defect repetition frequencies become very low and difficult to detect amongst background noise. c) very long time records need to be digitised and further processed. d) slowly moving structures are often very massive and stiff However it is often the case that the most critical aspect of an industrial process operates at the slowest speed and under the highest loads. Examples are mills (eg sugar, paper and steel), rotating kilns, settlement tank scrapers etc.. The associated machinery is usually highly specialised and represents a high capital investment. Because of this such machinery is a prime candidate to benefit from Condition Monitoring. In particular this has provided a strong impetus for researchers and product developers to devise innovative signal processing methods in an attempt to apply vibration based CM [eg Ratcliffe (1990), Murphy T., Strackeljan et. al (1999)]. Whilst it is not possible to conunent on the practicality, range of applicability or effectiveness of all such methods it is fair to say that at the present time end users in industry who work in the field of Condition Monitoring with slowly rotating machinery know that there is currently no simple way for them to apply vibration techniques. Although it may be that newer and more complex vibration based techniques can be widely effective on slowly rotating machinery this has yet to be adequately demonstrated to end users and because of this we have concluded that an unsatisfied requirement exists in industry which we have sought to address. Instead of pursuing an ever more complex approach to overcoming the low signal to noise ratios of the vibration technique our long term aim has been to create an easy to install, easy to interpret and low cost approach to monitoring such slow speed machinery. This paper describes the development of an AE based instrument to achieve these objectives for everyday use in industry by shop-floor personnel.
BACKGROUND TO AE FROM SLOWLY ROTATING MACHINERY For both impact and frictional source processes the signal strength at source reduces with increasing frequency. Because of this it is usual for AE sensors to be of a resonant design so that their output is magnified by the mechanical Q of the piezoelectric detection element. Typically reported detection frequencies for such AE sensors fall somewhere within the range 50 kHz to 500 kHz. By contrast vibration measurements usually use broadband accelerometers which are typically used in their region of flat frequency response well below the accelerometer resonance. Using appropriate AE instrumentation higher speed machinery gives rise to a readily detectable continuous AE signal with transient variations superimposed upon it due to such processes as momentary rubbing, slip-stick and discrete impacts from defects in surfaces which make contact in the loaded zone. In particular the high signal to noise ratio (SNR) of defect activity for AE compared with vibration is a recurrent theme very widely reported in the literature and is the principal reason why AE monitoring can be so successfully carried out in the time domain [Holroyd (1999), Holroyd (2000)]. For the case of very slowly rotating machinery the continuous AE signal typically drops below the limit of detectability and defect presence manifests itself as isolated bursts of AE activity as shown in the example in Figure 1. For very slowly rotating machinery the 'mark to space' ratio of the presence of detectable AE activity can be extremely low making some forms of signal processing inappropriate. However since each individual burst of activity is observable directly in the time domain signal it follows that defect detection need not necessarily be hindered by such low mark to space ratios.
30
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Figure 1 : AE envelope waveform from a defective slowly rotating bearing. When monitoring slowly rotating machinery it is important not to overlook the need to include a statistically significant sample of the waveform in the analysis. At very slow speeds this means very long time records. Although in principle it would be possible to digitally store very long samples of the AE waveform and post process the signals, in practice such an approach would (with current technology) be too costly and impractical for widespread use. Instead some form of on-line processing is required to autonomously reduce the information content of the signal whilst retaining that part of the information which is of principal interest. A wide variety of signal processing methods are commonly used in AE monitoring including distributions of AE parameters (eg peak amplitude in an amplitude distribution), locations plots (essentially distributions as a function of time difference of wavefront arrivals at an array of AE transducers) and trend plots of a processed AE parameter (such as AE count rate, energy etc.). For example Rogers [Rogers (1979)] positioned two AE sensors diametrically opposite each other on the inside face of a crane slew ring. The detected activity was presented in the form of an amplitude distribution and in the form of a linear location of its source position. In contrast Miettinen & Pataniitty [Miettinen & Pataniitty (1999)] used a trend of AE count rate as a means of providing long term monitoring of the AE activity from 16 wheel bearings rotating at 8 rpm and supporting a kiln. A quite different approach has been reported by Mba et. al. [Mba et. al. (1999), Mba et. al. (1999)] who has published extensive work describing the in-depth analysis of AE signals from Rotating Biological Contactor bearings rotating at speeds as slow speed as 0.6 rpm in terms of their Auto Regressive Coefficients. As a result of the lack of comparative data in the literature it is difficult to rank the relative merits of the different approaches that have been reported (and it will be seen that this paper is similarly deficient in this regard). Consequently it is difficult from an end-users perspective to know what method(s) to choose for particular applications. On the one hand it is self evident that no single signal processing approach can comprehensively describe all aspects of the AE activity from a slowly rotating bearing whilst on the other hand economics will dictate that all techniques cannot be simultaneously applied. In addition it is also apparent to the AE practitioner that the signal processing techniques described above generally involve a degree of operator dependency in setting up the signal processing as well as significant expertise in the interpretation of the output. Such techniques are good for the investigative or diagnostic stage of a monitoring exercise since an experienced operator will then be at hand. As an alternative approach our aim in the work reported in this paper was to try and be more pragmatic, accepting the limitations of a simpler signal processing procedure, yet providing a more direct and 31
readily understandable output. In this way we hoped to develop an affordable front-line condition monitoring tool for slowly rotating machinery which removed the need for an experienced operator but nevertheless was able to clearly identify when a machine had problems and enable a simple trend indicative of its deterioration to be generated. A NEW APPROACH Back in 1997 at Holroyd Instruments we considered the nature of the AE signals generated by slowly rotating machinery and developed new signal processing algorithms which we believed would be of relevance to monitoring very slowly rotating machinery. Although specific details of these algorithms are not made available for commercial reasons part of the external functionality of one of the parameters, 'Extent®', can be described as being to characterise the detectable AE activity in terms of the percentage of the rotational cycle where activity of concern is detected. In particular the software algorithm for Extent® was intentionally developed so that it does not require a once per rev signal from the machine since this facilitates easier application and retrofitting to existing machinery installations. The only information which needs to be inputted into this algorithm is the approximate period of rotation which is essential in order to ensure that a statistically significant length of signal is processed during the measurement.
APPLICATION EXAMPLES Initially the software algorithm for Extent® was incorporated within special versions of the MHCMemo portable CM instrument in order to allow third parties to evaluate its effectiveness on their slowly rotating industrial machinery. Some of the results of this 'beta-trialling' by third parties are described below: Turntable bearings This application is based in a foundry where there are a series of turntables which rotate at 6 rpm. Of the many readings taken on a large nimiber of bearings it was found that measurements had been made at various stages in advance of 10 catastrophic bearing failures.
Readings shown were taken over several months on 10 bearings.
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Figure 2 : Sensitivity of Extent® ' to proximity of failure in turntable bearings. 32
Figure 1 shows a composite plot for all the Extent® readings leading up to the failure of these 10 bearings. It is clear from this plot that Extent® provides a simple instant means of recognising the difference between a good and a defective bearing and is trendable up to the point of failure. Figure 2 also suggests that advanced warning of around 100 days might be expected for this particular application. Rotating Kiln support wheels This application is also based in heavy industry and concerns measurements made at the 8 wheel bearing positions of the 4 wheels supporting a rotating kiln. These wheels rotate at 7.5 rpm. Figure 3 shows three sets of readings for the Extent® value for measurements at each of the bearing positions. From the first two sets of readings, which were taken on the same day before and after greasing, it is clear that the reading at bearing #7 is noticeably higher. Simultaneous vibration diagnostics suggested (correctly as it turned out) there was no problem with this bearing and so it was decided to continue running. However 10 weeks after these measurements the wheel supported by bearings #7 and #8 catastrophically collapsed as a result of a fatigue crack grovdng in the axle shaft adjacent to bearing #7. The third set of readings were taken after the kiln assembly was rebuilt with a new axle. The previously high E value measured at bearing #7 has dramatically reduced to a more normal value. The reading at bearing #8 also shows a reduction.
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Figure 3 : Sensitivity of' Extent®' to shaft cracking in a wheel axle. In this application it is likely that the AE monitoring was picking up the crack closure and crack opening noise caused by rubbing of the interlocking fatigue crack faces. It isfiillyunderstandable why such activity would not be detected by vibration monitoring and illustrates well the benefits of signal detection methods that are not reliant upon pre-calculated defect repetition frequencies. Other applications Over the 4 year period in which this new slow mode of AE monitoring has been beta-trialled a number of other successes have been reported including sprocket bearings rotating at 2 rpm (detection of bearing damage and water ingress into the grease) and support bearings on cylinder dryers rotating at
33
approximately 10 rpm (a worn bearing was immediately picked out from a one-off set of measurements on 10 bearings during a sales demonstration). To date our experience is that bearings running normally and in good condition typically have Extent® values of less than 5. With this knowledge we have had good success in carrying out one-off measurements (ie sales demonstrations) and fmding rogue bearings on a wide range of very slowly rotating machines. In the light of our experiences and those of our customers who have been evaluating it, we have finally developed our first commercial product incorporating slow mode monitoring.
IMPLEMENTATION AS A STANDARD SYSTEM Although the demonstration trials described above were carried out with the analysis software implanted in a portable instrument it is clear that monitoring very slowly rotating bearings can be a time consuming and tedious task for the operator if a portable instrument is used. For this reason it was decided that the first product incorporating the new slow mode should be an on-line monitoring system which has the product name MHC-SloPoinf^^. This is a compact DIN rail mounting module which forms a complete monitoring system requiring only an external DC power supply and an external AE sensor. It has built in dual intelligent alarms and data logging. The data logging feature enables trends to be instantly observed whenever an alarm is triggered. In keeping v^th the concept of an easy to retrofit instrument the unit is able to be simply wired using screw terminals and takes a similar form to industry standard process monitoring and control modules.
DISCUSSION Clearly the currently reported signal processing approach deals with only one aspect of the AE signal and can be supplemented by or supplemental to other signal processing methods (such as those Referenced in this paper). This is because the work reported here has not been aimed at producing the most sophisticated AE instrument possible for monitoring slowly rotating bearings, nor producing a diagnostic tool for use by Condition Monitoring specialists. Instead the path has been deliberately chosen to investigate simpler to use and easier to interpret signal processing methods than those which have been previously adopted when applying either AE or Vibration techniques to slowly rotating machinery. An obvious concern when using the AE technique to monitor slowly rotating bearings is that background noise will mask the detection of defects. So far our experience is that defects are able to be readily detected even in heavy industry under noisy (and dirty) site conditions. Although it would be wrong to suggest that the monitoring system described here has total immunity to such noise we are confident that it is applicable in the majority of industrial uses for which it will be required. We further note that the measurements described were all spot readings (ie single measurements) and it is self evident that the use of more averaging over longer time periods would give a further improvement in signal to noise ratio. Different signal characterisations are likely to be of greater or lesser importance for different fault modes. In the examples to date the ' Extent® ' value has been the most widely relevant AE signal characterisation we have evaluated. However it is noted that on its own this parameter may well be inappropriate in the case of, say, a bearing race with an isolated crack in it. It is for this reason that a selection of different AE signal characteristics are required and a strategy should be adopted whereby alarms are set on each of them. The system that has been created, the MHC-SloPoinf^^, performs four such signal characterisations. Importantly its design pays great attention to consistency and long term 34
stability of these signal characterisations since this is very relevant to measurement integrity over the long periods it often takes for very slowly rotating machinery to degrade.
CONCLUSIONS 1
AE techniques can be successfully applied to monitoring the condition of very slowly rotating machinery.
2
A wide range of signal processing methods can be employed to detect the presence and amount of damage although cost and complexity limit their application.
3
A new signal processing method which is both simple to use and easy to interpret has been devised and tested over a 4 year period on numerous machines at a large number of test sites,
4
Indications are that such simple methods can be both effective and widely applicable for detecting the presence and amount of damage.
References Holroyd T. (1999). Acoustic Emission - Looking for a big change in a small signal, Proceedings of Integrating Dynamics, CM & Control for the 21st Century ISBN 90 5809 1120, published by Balkema, 499-502. Holroyd T. (2000), The Acoustic Emission & Ultrasonic Monitoring Handbook Coxmoor Publishing Company, ISBN 1 90189 207 7, 2000. Mba D., Bannister R. H., & Findlay G. E. (1999). Condition Monitoring of slow speed rotating machinery using stress waves - part 1. IMechE Jnl of Process Mechanical Engineering (part E), 213, 153-170. Mba D., Bannister R. H., & Findlay G. E. (1999). Condition Monitoring of slow speed rotating machinery using stress waves - part 2. IMechE Jnl. of Process Mechanical Engineering (part E), 213, 171-185. Miettinen J. & Pataniitty P. (1999). Acoustic Emission in Monitoring Extremely Slowly Rotating Rolling Bearings., Proceedings ofCOMADEM '99, ISBN 1 901892 13 1, published by Coxmoor, 289297. Murphy T. (year unknown), The development of a data collector for low-speed machinery. Reference unknown. Ratcliffe G. A. (1990). Condition Monitoring of rolling element bearings using the enveloping technique. Proceedings of IMechE seminar 1990-1 Machine Condition Monitoring, ISBN 0 85298 712 9, 55-65. Rogers L, M. (1979). The appHcation of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings. Trihology International, April, 5159.
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Strackeljan J., Lahdelina S., Vuoto V. & Behr D.(1999). Vibration monitoring of slowly rotating bearings using higher derivatives and a fuzzy classifier. Proceedings of Condition Monitoring '99, ISBN 901892115, published by Coxmoor, 375-386. Acknowledgements Extent® is a Registered Trademark of Holroyd Instruments Limited. Patents pending on signal processing methods and means described in this paper.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
MONITORING LOW-SPEED ROLLING ELEMENT BEARINGS USING ACOUSTIC EMISSIONS N. Jamaludin\ and D. Mba^ 'Dept of Mechanical and Material Engineering, Faculty of Engineering, UKM, 43600 Bangi, Selangor, Malaysia. ^School of Engineering, Cranfield University, Cranfield, Beds. MK43 OAL.
ABSTRACT The most established technique for monitoring the integrity of rolling element bearings is vibration analysis. However, this success has not been mirrored at low rotational speeds of less than 20 rpm. At such speeds the energy generated from bearing defects might not show as an obvious change in signature and thus become undetectable using conventional vibration measuring equipment. This paper presents an investigation into the applicability of acoustic emission for detecting early stages of bearing damage at a rotational speed of 1.12 rpm. Furthermore, it reviews work undertaken in monitoring bearings rotating at speeds below 20 rpm. Investigations were centered on a bearing test-rig onto which localised surface defects were seeded by spark erosion. Analysis of acoustic emission signatures associated with bearing outer race, inner race and roller defects, showed that the uniqueness of the defect signature patterns could be utilised to provide early fault diagnosis. KEYWORDS Acoustic emissions, auto-regressive coefficients, K-means clustering, slow-speed rotating machinery, rolling element bearings, spark erosion. INTRODUCTION Monitoring bearing degradation by vibration analysis is an established technique and the various methods of analysis have been widely published (McFadden [1990], Ractliffe [1990], Harker et al [1989], Bannister [1985], Setford [1992], Berry [1992]). However, at low-rotation speeds there are numerous difficulties that have been detailed (Canada et al
37
[1995], Murphy [1992], Robinson et al [1996]). The main problems include selecting the optimum measurement parameter, instrument limitations and sensor requirements. REVIEW OF MONITORING TECHNIQUES APPLIED TO BEARINGS ROTATING AT LESS THAN 20RPM. Canada et. al. (1995) developed a Slow Speed Technology (SST) system for measuring vibrations on low-speed rotating machinery. It was based on separating the high frequency noise of the machine from the low frequency signatures of interest, furthermore, claims that this method could be applied at speeds as low as 10 rpm were made. Robinson et. al (1996) built on the SST method described earlier (Canada et. al. [1996]). More relevant to this paper is the research on acoustic emission (AE) and its applicability for monitoring low-speed bearings. Sato (1990) investigated the use of AE to monitor low-speed rotating bearing damage by simulating metal wipe in journal bearings at 5.5 rpm. It was observed that acoustic bursts were generated as a result of slight metallic contact and the amplitude of the waveform became larger with increasing metal wear. McFadden et. al. (1983) used AE sensors to monitor a fault that was simulated by a fme scratch on the inner raceway of a bearing rotating at 10 rpm. The AE transducer appeared to respond to minute strains of the bearing housing caused by the concentrated loading of each ball passing the defect. For this investigation, acoustic emissions are defined as the transient waves generated by the interaction of two surfaces that are in relative movement, in this instance, rubbing between metal surfaces in contact (Green [1955]). This makes it an ideal tool for application to condition monitoring of low-speed rotating rolling element bearings. TEST-RIG AND MEASURING EQUIPMENT A bearing test-rig was designed onto which surface defects were seeded by spark erosion. The rig consisted of a motor/gearbox system, two support slave bearings, a test bearing and a hydraulic cylinder ram, see figure 1. The motor provided a rotational speed of 1.12 rpm. The test bearing was a split Cooper spherical roller, type 01B65 EX, with a bore diameter of 65mm and 12 rollers in total. This type of bearing was chosen due to its ability to be disassembled without removing the slave bearings, thereby allowing easy assess to the test bearing. The support bearings were of a much larger size than the test bearing. A radial load of 55KN was applied to the top of the test bearing via a hydraulic cylinder ram supported by a 'H' frame. A high viscosity grease, lOOOCst at 40°C, was used in the test bearing. The process of data acquisition involved fixing a receiving transducer onto the test bearing. A schematic diagram illustrating the data acquisition system used throughout all experimental tests is shown in figure 2. All instrumentation employed was supplied by PAC (Physical Acoustics Corporation). A commercially available wide-band piezoelectric type sensor (type WD) with an operating frequency range between 100 kHz and 1000 kHz was used. A dual-channel 8-bit analogue-to-digital converter (ADC) Rapid Systems R2000 was used for data acquisition. The electronic noise level on the ADC
38
system, with 60dB amplification, had a peak voltage of 30 mV. The sampling rate employed for all tests was 5MHz. 510mm
Hydraulic ram
bearing Motor/gear box unit
280mm 540mm Figure 1
Schematic diagram of the bearing test-rig
Acoustic emission sensor, lOOkHztolMHz
^
W
Pre-amplifier, 60 dB gain
r COMPUTER Post processing
Figure 2
^ ^
Analogue-to-digital converte r (ADC)
Post-amnlifier and
povver source for pre- amplifier
Schematic diagram of data acquisition system
EXPERIMENTAL PROCEDURE AND RESULTS Defects seeded by spark erosion on the outer and inner races, and on a roller element, (22, 23, 24, and 25) resulted in surface damage that resembled pitting. The defect size on each component was approximately 3,0mm wide and an effective depth of 75|im. Operational baseline measurements To obtain operational background noise, the test-rig was run without any seeded fault. Continuous data was recorded with a pre-trigger level set above 30mV, A typical signature with a corresponding frequency spectrum can be seen in figure 3. Observations and analysis showed that the maximum amplitude for this condition did not exceed
39
170mV. Therefore, on tests with seeded defects the pre-trigger level was set slightly above 170mV in order that AE's associated with defects could be measured. Time aignaiure of oilier race defect
Time signature of a good bearing
0.1
0.2
0.3
0/4
0.5
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0.7
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ai
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Figure 3
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0.8
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Typical time signature with corresponding frequency spectrum for a good bearing and outer race defect
Seededfault simulation (Spark Erosion) Faults seeded with this technique resulted in acoustic emission activity above the operation baseline trigger level (170mV). The signatures were correlated to the fault condition by monitoring the position of bearing components at the time of acquisition, i.e., signatures were generated only when the seeded fault was within the loaded region of the bearing. Typical AE signatures for the various defect conditions, with corresponding frequency spectra, are displayed in figures 3 to 4. AE signatures for each simulated fault condition were recorded for several revolutions of the shaft until 30 data sets were obtained. Time signature of roiier defect
Time eignature of inner race defect
ai
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Figure 4
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Typical time signature with corresponding frequency spectrum, inner race and roller defect
40
CLASSIFICATION AND SIGNAL PROCESSING Typical acoustic emission features, amplitude and energy, were employed to identify and classify AE signatures associated with seeded defects and background noise. The maximum amplitude and energy of each AE signature were extracted and plotted against the associated AE signature, see figures 5 and 6. To aid interpretation, a polynomial fit based on a 5^ order model was applied. Peak Amplitude: Defects and good bearing • • 4+
5
10
IS
20
Outer race defsct Inner race defect RoTler defect Good
25
30
Individual AE signature Figure 5
Peak amplitude values for all defects
This classification process was applied to experimental data where all the influencing factors are controlled. It was therefore though prudent to establish a relatively more robust technique for classifying and grouping the various fault conditions. The philosophy behind this was that on-site, the interpretation of signatures from real operational bearings might not be distinguishable by extracting parameters such as amplitude and energy. This is due to the very random nature of noise that could be generated on bearings operating under varying loads and environments. Since the defects simulated in this paper originate from different parts of the bearing, the associated AE signature will have "^characteristic features that are unique to their particular transmission path. As it has been shown (Mba et al [1999]) that AR coefficients can represent the shape of a signature, classification based on an AR model was undertaken.
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Energy: Defects and good bearing
0
5
10
15
20
Individual AE signature
Figure 6
Energy values for all defects
The computation of AR coefficients is derived from linear prediction and a review of parametric models such as AR has been detailed (Kay et al [1981], Makhoul [1975], Haykin [1984]). Application of the Forward Prediction Error and Akaike Information Criteria (26,27) aided in selection of the optimal AR model order; a 15^'' order model was employed. The process of classifying these coefficients employed a cluster technique known as K-means (Everitt [1974]). This is a non-hierarchical technique that measures the Euclidean distances between the centroid value of the AR coefficients associated with each signature. The resuhs were displayed on dendrograms (Everitt [1974]). AutoRegressive coefficients associated with AE signatures from inner, outer and roller defects were compared with those from operational background noise by clustering, see figures 7 to 9. Furthermore, signatures from all fault conditions, and background noise, were mixed and clustered, see figures 10 to 12. Figures 11 and 12 are close-up views of figure 10.
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Classification using the centroid value of AR coefBcients, ORD and noise Ml
o - Outer race defect
SIS
n - noise 022
Cluster 2 Outer race defect signatures
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i
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Figure 8
Classification of AE's associated with background noise and outer race defects. Classification using the centroid value of AR coefficients, RD and noise
Euclidean distance between centroid values of AR coefficients
Figure 9
with individual AE's
Classification of AE's associated with background noise and roller defects.
43
Classifkation using the ceatroid value of AR coefficients, All defects and noise
0
0.1
0.2
0.3
0.4
0.5
0.«
0.7
0.8
0.9
Euclidean distance between centroid values of AR coefficients associated with individual AE's
Figure 10
Classification of AE's associated with background noise and all fault conditions, see figures 11 and 12 for close-up view of clusters 1 and 2 respectively.
DISCUSSIONS Acoustic emissions emitted from defect simulations were attributed to the relative movements between mating components. Signatures associated with operational baseline data for the test-rig showed the maximum amplitude to be in the order of 170mV. By setting a trigger level above this value, and undertaking individual fault simulations, only signatures relating to a specific fault condition were captured. During simulation of the outer race defect (ORD), AE activity occurred approximately every ten seconds. This corresponded to the calculated outer-race passage frequency of 5.65 rpm. Furthermore, AE signatures associated with the inner-race defect (IRD) and roller defect (RD) were only detected for a certain period of time, i.e., did not occur continuously throughout one complete revolution of the cage. This phenomenon was due to the fact that AE was only generated when the IRD and RD were in the loading zone. Comparisons of the amplitude and energy levels associated with individual AE's showed trends that were distinguishable. The lowest amplitude and energy values were associated with operational noise. Furthermore, a gradual increase in amplitude and energy levels was evident for inner-race, roller and outer-race defects respectively. A polynomial fit was employed to highlight this trend.
44
Classification using the centroid value of AR coefficients, All defects and noise
o - Outer race defect i • Inner race defect r - Roller defect n - noise
Cluster 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Euclidean distance lictween centroid values of AR coefficients associated with individual AE's
Figure 11
Close-up view of cluster 1 in figure 10
The increasing values of amplitude and energy were attributed to the closeness of the source of defect to the sensor. For instance, as the sensor was placed on the bearing housing, it was expected that the greatest amplitude/energy of AE should come from the outer race (under constant load conditions). Signature from the inner race defect had more interfaces to overcome before reaching the receiving sensor, thus it was not surprising that attenuation has played a vital role in reducing its strength. Acoustic emission values of amplitude and energy emitted by the roller defect were scattered between corresponding levels of the inner and outer race and was attributed to the position of roller defect at the time of data acquisition, for instance, a higher level of amplitude/energy was expected when the roller defect made contact/rubbed with the outer race. This mechanism explains the scatter of roller defect amplitude and energy values between the inner and outer race values. Whilst the results already presented clearly show that extracting amplitude/energy values from AE could help indicate bearing deterioration for a particular bearing type, a more robust system of classification was investigated, as explained earlier. Clustering of AR coefficients associated with background noise did not result in any clear grouping, indicative of the random nature of noise. By comparing classifications of background noise against individual fault simulations, it was evident that the AR cluster technique showed two distinct groupings.
45
Classification using the centroid value of AR coefficients. All defects and noise o - Outer race defect i - Inner race defect r - Roller defect n - noise
Inner race and roller group
Cluster 2
Noise group
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Euclidean distance between centroid values of A R coefHcients associated with individual AE's
Figure 12
Close-up view of cluster 2 in figure 10
The classification of all defect simulation signatures with background noise resulted in well-defined clusters. These clusters showed background noise to be quite distinct (see figure 11), however, there was some overlap between inner and outer race and roller defect signatures. Within cluster 2, figure 12, two clear groups were distinguishable; the first comprised soley noise signatures and the second was predominately a mixture of inner race and roller defect signatures. Cluster 1 of figure 11 was predominantly a group of outer race and roller defect signatures. This mixture of signatures can be attributed to the position of the roller defect at the time of data acquisition. Thus, a roller defect signature generated due to rubbing against the outer race will have characteristics that mirrored closely to the outer race defect signature. With the exemption of a few signatures, most roller defect signatures were grouped with either inner or outer race defect signatures. It is interesting to note that this phenomenon was also observed with amplitude and energy classification. Given the scenario of an operational bearing with a fault, it is probable that AE signatures measured could contain background noise that was of similar amplitude/energy levels as the fault signature, consequently, we would be unable to differentiate the signatures by observing amplitude and energy values. However, because operational background noise is random in nature, the shape of its signatures will also be random. However, the shape of AE signatures associated with a fault would generally be of similar pattern. Thus, if
46
clustering of the AR coefficients associated with all AE signatures yielded distinct group clusters, this would be evident of early signs of deterioration. If no distinct groups were evident, the bearing would be passed as defect free, as clustering of AR coefficients associated with random shaped signatures (background noise) will result in no clearly defined groups. CONCLUSION Investigations into the application of the acoustic emission technique to condition monitoring of low-speed rotating element bearings have proven successful. Results of the seeded mechanical faults on the test-rig showed that acoustic emissions were generated from rubbing of mating components. Classification of defect signatures with autoregressive (AR) coefficients has been shown to aid in determining the mechanical integrity of the bearing. Furthermore, typical AE parameters such as amplitude and energy can provide valuable information on the condition of a particular low-speed rotating bearing. REFERENCES Bannister, R. H. (1985). A review of rolling element bearing monitoring techniques. Instn Mech Engrs conference on condition monitoring of machinery and plant, pp 11-24. Berry, J. E., (1992). Required vibration analysis techniques and instrumentation on low speed machines ( particularly 30 to 300 RPM machinery ), Technical Associates of Charlotte Inc., Advanced Vibration Diagnostic and Reduction Techniques. Canada, R.G., and Robinson, J.C., (1995). Vibration measurements on slow speed machinery. Predictive Maintenance Technology National Conference (P/PM Technology), Vol. 8, no. 6. Indianapolis, Indiana, pp 33-37. Everitt, B. (1974). Cluster analysis. Published on behalf of the Social Science Research Council by Heinemann Educational Books New York: Halsted Press. ISBN 0 435 822977. Green, A. P. (1955). Friction between unlubricated metals: a theoretical analysis of the junction model. In Proc. Of the Royal Society of London, A, Vol. 228. pp 191-204. Haykin, S. 1984 Introduction to adaptive filters. Macmillan Publishing Company, New York. ISBN 0 - 02 - 949460 - 5. Harker, R. G. and Sandy, J. L. (1989). Rolling element bearing monitoring and diagnostics techniques. Transactions of the ASME, Journal of Engineering for Gas Turbines and Power. Vol. 111. pp 251-256 Kay, S.M, and Marple, S.L Jr. (1981). Spectrum analysis - A modern perspective. Proceedings of the IEEE, Vol. 69, No. 11. pp 1380-1419 Makhoul, J. 1975 Linear prediction: A tutorial review. In Proc. Of the IEEE, Vol. 63, No. 4. pp 561-580. Mathew, J. and Alfredsoa, R.J. (1984). The condition monitoring of rolling element bearings using vibration analysis. Journal of Vibration, Acoustic, Stress and Reliability Design, Transactions of ASME, Vol. 106. pp 447-453. Mba, D., Bannister, R.H., and Findlay, G.E. (1999). Condition monitoring of low-speed rotating machinery using stress waves: Part I. Proceedings of the Instn Mech Engrs, Vol. 213, Part E.pp 153-170,
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Mba, D., Bannister, R.H., and Findlay, G.E. (1999). Condition monitoring of low-speed rotating machinery using stress waves: Part H. Proceedings of the Instn Mech Engrs, Vol. 213, Part E.pp 171-185, McFadden, P. D. (1990). Condition monitoring of rolling element bearings by vibration analysis. Proceedings of the Instn Mech Engrs seminar on machine condition monitoring, pp 49-53. McFadden, P. D., and Smith, J. D. (1983). Acoustic emission tranducers for die vibration monitoring of bearings at low speeds. Report no, CUED/OMech/TR29, Muq)hy, T.J., (1992). The development of a data collector for low-speed machinery. 4th international Conference on Profitable Condition Monitoring, hK Group Ltd., 8-10 Dec, Stratford-upon-Avon, UK. pp 251-258. Ractliffe, G. A. (1990). Condition monitoring of rolling element bearings using enveloping technique. Proceedings of the Instn Mech Engrs seminar on machine condition monitoring, pp 55-65. Robinson, J.C, Canada, R.G., and Piety, R.G. (1996). Vibration Monitoring on Slow speed Machinery: New Methodologies covering Machinery from 0.5 to 60Qq)m. Proc. 5th International Conference on Profitable Condition Monitoring - Fluids and Machinery Performance Monitoring, pp 169-182, brf Group Ltd., Publication 22, Harrogate, UK. Sato, L (1990). Rotating machinery diagnosis with acoustic emission techniques. Electrical engineering in Japan, Vol. 110, No. 2. pp 115-127. Setford, G. A. W. (1992). Bearings-condition monitoring, condition measurement and condition control. 4th international Conference on Profitable Condition Monitoring, bJf Group Ltd., pp 231-240,8-10 Dec., Stratford-upon-Avon, UK.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
CONDITION MONITORING OF ROTODYNAMIC MACHINERY USING ACOUSTIC EMISSION AND FUZZY C-MEAN CLUSTERING TECHNIQUE T Kaewkongka, Y H Joe Au, R T Rakowski and B E Jones The Brunei Centre for Manufacturing Metrology, Brunei University, Uxbridge, Middlesex UB8 3PH, UK Phone: (+44) 01895 274000 ext. 2608, E-mail:
[email protected] ABSTRACT This paper describes a method of bearing condition monitoring using the fuzzy c-mean clustering technique applied to the pre-processed acoustic emission parameters. Acoustic emission (AE) events were detected by a piezoelectric transducer mounted on the bearing housing of a test rig. AE parameters were extracted from the events and used as characteristic features to represent a machine operating condition. In this experiment, four machine conditions that may happen to a rotodynamic machine were investigated and they corresponded to (a) a balanced shaft, (b) an unbalanced shaft, (c) a shaft with misaligned supportive bearings and (d) a shaft running in a defective bearing. During training, the fuzzy c-mean clustering technique was applied to establish the centres of the four clusters. For testing, a minimum distance classifier was used to classify an AE event from an unknown condition into one of the four conditions. The recognition rate was 97.22 percent. KEYWORDS Acoustic emission, condition monitoring, ftizzy c-mean clustering technique, minimum distance classifier, rotodynamic machinery. INTRODUCTION Rolling element bearings are perhaps the most ubiquitous machine elements in engineering as they can be found in almost all-rotating machines. With ever growing competition in industry, tiiese bearings are considered critical components because any malfunction, if not detected in time, leads to catastrophic failure and hence losses due to machine downtime and other forms of damage. It is evident that a reliable condition monitoring system is highly desirable so that it will reduce the cost of these consequences and enhance the overall equiprnent effectiveness. Basically there are two approaches to bearing maintenance: (1) statistical bearing life estimation and (2) bearing condition monitoring and diagnostics [1]. Statistical bearing life estimation predicts the
49
fatigue life of a bearing. However, its application has limitations, since unusual operating conditions often occur and can severely decrease a bearing's life. In this situation, estimating a bearing's life based on standard operating conditions is unrealistic. The other approach - bearing condition and diagnostics - can be more reliable because it gives up-to-date information about the condition of a bearing. The more popular condition monitoring techniques for bearings are based on vibration and acoustic emission analyses. Previous research [2,3,4] has demonstrated that AE monitoring is superior to vibration monitoring in that the former can detect subsurface crack growth whereas the latter can at best detect a defect only when it emerges on the surface of a structure. Acoustic emission (AE) is a natural phenomenon of sound generation in a material under stress. If the material is subjected to stress, a sudden release of strain energy takes place in the form of elastic wave. Each release of energy results in an AE event which often lasts no longer than a millisecond. A rotation bearing can produce AE events each time a surface defect comes into contact with other elements. These AE events are high-fi-equency transients with frequency components typically in the range from 100 kHz to 1 MHz. An AE event is characterized using parameters such as ring-down count, rise time, event duration, energy and peak amplitude. A threshold is used in order to ehminate 'noise' and only events that rise above the threshold are counted. Evidently, the threshold level affects the value of some of these parameters. A typical example is the event duration. By definition, it is the time that the envelope of an AE event is above the threshold. When the threshold level is high, the time will be shorter. The peak amplitude of an AE event is the maximum excursion of the corresponding voltage signal from the zero level. The energy of an AE event is the energy contained in the corresponding voltage signal and, strictly speaking, is not the true energy of the event itself. Energy is calculated using the formula T
Energy o.i&.M
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Figure 4 - Tabular FMECA of a F-lOO Fuel System As with traditional a FMECA, the failure mode is provided along with its effects (ranked from top to bottom as primary, secondary, tertiary, etc.). The Criticality or Frequency of Occurrence of the failure mode is ranked from A to E where: A = Frequent, B = Probable, C = Occasional, D = Remote, E = Improbable In practice, this Criticality letter would be associated with a specific probability of failure range. The Severity of the failure mode is ranked from I-IV where: I - Catastrophic, II-Critical, III - Marginal, IV - Negligible The first FMECA enhancement is that failure mode symptoms have been added to the "effects"column and are shaded in blue (or light gray). Failure mode symptoms are events that can be observed prior to the failure mode occurring or when the failure mode is in a very early stage of development. Subsequent effects may or may not be downstream failure modes. In the case where an effect is a downstream failure mode, the failure mode of focus could be considered a failure mode precursor. The "Component" column identifies the component immediately affected by the failure mode while "Module" is the subsystem in which the component resides. This fimctional relationship is crossreferenced with the functional block diagram. In a similar fashion, the "Sensor" column lists the sensor that can observe the symptom or effect while "S_ModuIe" is the subsystem in which the sensor resides and "S_Component" is the conqjonent it is linked to. All sensors in this example are required for control or safety purposes. Finally, *T)iagnostics" and "Prognostic" column have been added. The 'Diagnostics" column describes if there are any discrete diagnostic (Built in Test (BIT)) or continuous processing algorithms that can observe the symptom or effect. The "Prognostics" column describes any prognostic algorithms that can be used to obtain a RUL prediction on the failure mode.
RESPONSE MODELS In some cases, a model of a subsystem may be developed that can provide valuable insight into where sensor are likely to have the most observational quality on failure modes. This optional level of
79
fidelity allows for detailed, physics-based subsystem modeling, to be used for examining PHM tradeoffs. Such trade-offs at this level would include analyzing the number of sensors required, location of the sensors and associated algorithms. This type of model would be integrated in tfie overall HM design environment thus far discussed where cross-system influences can be examined and accounted for (Figure 5). Integrated System HM Model
Physics-Based FM Response Model
>Accounts for Systcm/Subsystem Interactions >Evaluatcs overall HM design cost/benefit and design tradeoffs
Yields detailed HM requirements and optimal sensor selection and placement for a subsystem or subsystems
Figure 5 - Response model integratioii in the overall HM model One such system response model for a hydraulic system developed by Dr. Jacek Stecki et al. of Monash University is shown in Figure 6. This model illustrates how the system model may be perturbed to simulate how the effects of certain modes propagate in time and space. Sensor / algorithm combinations can be examined for their abihty to detect the perturbations.
ponent
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Scheduled Hardware Repl. Cost (SHRC) Scheduled Downtime (SD) Scheduled Labor Cost (SLC) Unscheduled penalty factor (UPF)
Figure 7 - Short Ust of HM attributes Sensors
Sensors are defined in the model as components for measuring physical quantities such as temperatures, pressures and currents. The ''Observational Quality" attribute of a particular sensor is a measure of the sensitivity with which it is able to pick up a physical signal linked to a particular failure mode. For example, an accelerometer stud mounted on top of a bearing casing may have a better observational quality than one magnetically mounted some distance away. Diagnostic and Prognostic Attributes Diagnostics can be either discrete or continuous. Discrete diagnostics are traditionally algorithms that produce 0 or 1 depending on if a threshold has been exceeded. Many types of Built In Tests (BITs) can be classified as Discrete Diagnostics. An example of a discrete diagnostics is an Exhaust Gas Temperature (EGT) reading that has exceeded a predetermined level. Continuous diagnostics are algorithms designed to observe transitional effects and diagnose a failure mode based on the method and rate in which the effect is changing. Continuous diagnostics are usually associated with observing the severity of failure mode syn:q)toms. Examples of continuous diagnostics would be a spike energy monitor for identifying low levels of bearing race spalling or an A.I. classifier for diagnosing that a valve is sticking. The "Detection Confidence score (0-1) (DDC)", and **% false positive score (0-1) - (DFP)" can be used to simultaneously account for truenegative and true-positive characteristics. Finally, Prognostic algorithms can use a combination of sensor data, a-priori knowledge of a failure mode and diagnostic information to predict the time to a failure or degraded condition with confidence bounds. Prognostic algorithms are linked directly to failure modes in the graphical FMECA model. Prognostics do not have an attribute associated with false alarms. The "Prognostic Accuracy" accounts for the early detection quality of the technology. A physical prognostic model (i.e. based on an FE
81
model) would ideally have a higher prognostic accuracy than an experienced-based model (i.e. WeibuU distributions of historical failure rates). More details on model fidelity are discussed in [2]. A valid concern is how the technical attributes of diagnostic and prognostics technologies can be determined. One method is addressed in [1], whereby algorithms are test objectively fi"om performance and effectiveness standpoints using transitional run to failure data. Of course in the absence of this type of information, and with a new sensor/algorithm combination, an educated guess may be the only option.
COST FUNCTION The health management design environment configuration and attributes contain a sufficient amount of information to generate and evaluate a "fitness" fimction. This fitness function is of the form: For each Failure Mode - FM(i) Step 1) Probability of Failure * Severity ^Consequential Cost ofFM(i) -^(Downstream Failure Mode Consequential Costs) * Probability of Propagation Step 2) *HMrisk reduction attributed to FM(i) Step 3) + Cost associated with False Alarms on FM(i) Step 4) + Total Cost of all HM technology The Consequential Cost (CC) is die sum of the direct and indirect costs required to address a particular fault/failure mode (i.e. repair, replace, inspect) ranging from quantifiable repair and labor costs, to less concrete costs such as the effect on system availability. Clearly, only a small aspect of all the possible factors are addressed here and it is purposely left ambiguous. If the probability of failure multiplied by consequential costs is defined as risk, health monitoring reduces risk by providing a probability that a particular failiu-e mode can be prevented by 1) either detecting an '^upstream" fault/failure mode or 2) prognosing when a fault/failure mode will occur. Unfortunately, the health monitoring adds development and hardware costs as well as the potential for false alarms. At the system-wide level, the benefits of the health monitoring technologies in terms of risk reduction must offset the costs and risk of the technology addition. Specifically, the formulation is as follows (using the acronyms defined in Figure 7): Steps 1 and 2 =
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HM DESIGN OPTIMIZATION The goal of the HM system optimization is to maximize the risk reduction provided by the design while minimizing costs. The optimization of the previously described cost function will operate between two boundaries; a "maximum" HM system configuration that includes the "wish list" of all potential sensors and associated algorithms that achieve complete failure mode coverage and a "minimum" configuration that is necessary for safety and control. The optimization algorithm will examine random configuration variations and calculate the "fitness" or cost for each. A genetic algorithm optimization scheme was chosen for the HM optimization because genetic algorithms are better configured to handle optimization problems with little regard for non-linearity, dimensionality or function complexity in general. Potential cost functions generated in the HM environment can include hundreds of independent variables and thus makes it impractical to utilize traditional optimization techniques such as gradient decent or other derivative-based algorithms. While the details of the optimization are outside the scope of this paper, it is important to note that there will be no clear "winner", rather many different HM system configurations will be suggested that the designer can evaluate on the basis of additional criteria. More on this subject can be found in [7]. COLLABORATIVE DESIGN ENVIRONMENT Before an example is given, it is important to address the design environment and associated architecture to enable the entire process. A collaborative work environment is being implemented in this program to allow a number of domain experts to operate applications from different locations, potentially on different operating systems, while sharing and maintaining the same data. For instance, the HM Design Tool will be used to perform advanced con^onent simulation models, FMEA and Cost/Benefit Models simultaneously at various locations. By utilizing the Intemet and standard data formats such as XML, data and applications will be accessible individually through web-based servers, and managed through an integration layer, which will control the communications protocol and access privileges (Figure 8).
83
Figure 8 - Design of Collaborative Work environment HM DESIGN EXAMPLE A simple, yet realistic example of a Health Management design evaluation is shown next. In this example, an electrically actuated control valve concept is addressed for an aerospace application. Recall that a HM design model has many hierarchies ranging from the component level to the system level. For brevity, this example will consider, but not illustrate, the far-reaching system effects of various valve failure modes nor will the cost function for this model be complete. The purpose of the example is to introduce the HM design and optimization process. The top portion of Figure 9 shows a Line Replaceable Unit (LRU) level Functional model of a Load Control Valve (LCV) that is used to regulate discharge air from an Auxiliary Power Unit (APU). Compressed air from the APU is used for main engine starts, environmental control and several other functions. The "in" and "out" bars on the left and right of the model are used to propagate signals, flows, and effects between levels.
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4.3 Wavelet model based analysis When a discrete wavelet transform (WDT) is applied in the analysis of the collected signal, the effect is not as high as what is expected. Figure 4 shows the DWT results, where the wavelet is chosen to be Daubechies 5 and the level is 4. Figure 4(a) is the normal signal or model output signal and its DWT results. Figure 4(b) is the response signal in 65°C and its DWT results. Altiiough it can be found seriously faulty infigure3, one is difficult to observe the difference by comparingfigure4(b) to figure 4(a). It is impossible to give any threshold due to the boundary effects. This tell us that the wavelet analysis alone is not quite suitable for the fault detection in control systems.
166
However, if the wavelet model based approach is applied, the effect is much obvious. Figure 5 and figure 6 show the results. In each figure, figure (a) shows the low frequency reconstruction of the residual in level 2, whereas figure (b) shows the high frequency reconstruction of the residual in level 2. In figure(c), the reconstruction of the overall residual is given out. In order to stress on the fault detection, a square signal entitled wavelet-based square (WBS) residual reconstruction is used. Two thresholds are pre-defined. The lower one is warning threshold for recipient detection. The upper one is for fault alarm when the fault goes seriously. (3)
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When the system goes healthy, the performance v^ll keep normal, and the WBS residual will keep zero (not shown here). As soon as the system performance varies, the WBS residual will give difference information. If the residual does not exceed the lower threshold, the system is referred to as normal. When the residual exceeds the lower threshold but lower than the fault alarm threshold, a warning will be sent out to call operator's attention. Figure 5 shows this case, which shows that an incipient fault may occur and you'd better keep an eye on it. When the WBS residual exceeds the upper threshold, an alarm will be sent out. It means the system is in faulty condition. Figure 6 shows this case, which tell us the condition is serious and a proper measure is necessary. 5 CONCLUSION This paper describes the diagnostic method based on the combination of the wavelet transform and the model based fault diagnosis approach. From the theoretical and practical understanding, the following conclusions can be drown. (1) Discrete wavelet transform is more suitable for the signal processing and fauh detection in control systems; (2) Wavelet analysis alone is less effective when applied for fault detection in control systems; 167
(3) Wavelet-model based approach (WMBA) is developed for the fault detection of an electrohydraulic servo system. It is proved that this approach is sensitive both in incipient faults and serious faults. It is more effective fault diagnostic tool. (4) New concepts have been developed. One is the wavelet based square (WBS) residual, the other is the collected system (CS) model. These new concepts are helpful for the model based fault diagnosis approach. REFERENCS A. El-Shanti, Z. Shi, F. Gu and A. D. Ball (2001). Dispelling the Rumors About Model-Based Diagnostics, Maintenance and Reliability Conference, USA. Al-khalidy, A, et al (1997). Study of health monitoring systems of linear structures using wavelet analysis, ASME, Pressure Vessels and Piping Division (PVP), Vol. 347, 49-58. Aretakis, N, and Mathioudakis, K. (1996). Wavelet analysis for gas turbine fault diagnostics, ASME paper. Atkinson R.M., et al (1996). Fault diagnosis in electro-hydraulic systems using neural networks, 5^^ International Conference on Profitable Condition Monitoring, UK, 275-285. Frank P.M. (1996). Advances in observer-based fauU diagnosis in dynamic systems. Engineering Simulation, Vol. 13, 716-760. Gertler, J.(1991). Analytical redundancy methods in fault detection and isolation, IFAC symposia, 921. Hogan, P. A, et al (1996). Automated fauh tree analysis for hydraulic systems. Trans. ASME, J. of dynamic systems, measurement and control. Vol 118, 279-282. Isermann R and P. Ball (1997) Trends in the application of model-based fauh detection and diagnosis of technical processes. Control Engineering Practice, Vol. 5,709-719. Le TT and J Watton (1998). Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks, Proc. Instn. Meek Engrs, Vol. 212, part I, 87-97. Lu, C.J, Hsu, Y.T. (2000). Application of wavelet transform to structure damage detection. Shock and Vibration Digest, Vol. 32, 50. Rainer Oehler, et al (1997). Online model based fault detection and diagnosis for a smart aircraft actuator, IFAC symposium, 591-596. Ren, Zhang, et al (2000). Fault feature extracting by wavelet transform for control system fault detection and diagnosis. Proceedings ofIEEE Conference on Control Application, Vol. 1, 485-489. Suh, C. Steve, et al (2000). Wavelet-based technique for detection of mechanical chaos. Proceedings ofSPIE'The International Society for Optical Engineering, 267-274. Yen, Gery Y, and Lin, Kuochung (1999). Wavelet package feature extraction for vibration monitoring. Proceedings of the InternationalJoint Conference on Neural networks. Vol. 5, 3365-3370. Zhanqun Shi, et al (2000). Kalman filter and its improvement used in fault signature of the machatronic control systems. Proceedings ofMARCON 2000, 4101-4108.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
ADVANCED FAULT DIAGNOSIS BY VIBRATION AND PROCESS PARAMETER ANALYSIS U. Stidmersen, O. Pietsch, C. Scheer, W. Reimche, Fr.-W. Bach Institute of Materials Science, Department of NDT, Appelstr. 11 A, D-30167 Hannover, Germany
ABSTRACT Due to the liberation of international markets, the economic success of power generation, process and fabrication industries depends on the availability, reliability and the efficiency of related key components. Maintenance can account for an extremely large proportion of the operating costs of machinery. Additionally, machine breakdowns and consequent downtime can severely affect the productivity of factories or the safety of products. Therefore, the manufacturing industries of the 2V^ century are influenced by the following major subjects of increased availability of physical resources, the improvement of product quality, and the improvement of manufacturing methods and techniques at high yearly utilization rate. Base is the effective use of available information contents from the machines in operation, like measured vibration signals and process parameters to extract significant patterns with the aim of early fault detection, the discovery of lack in the organization leadership and/or the operating condition. The presented case studies prove the success of the fault detection combining vibration measurements and process parameter analysis at different turbines. KEYWORDS vibration analysis, fault detection, signal processing, predictive maintenance INTRODUCTION The present state in the development of industrial plants is marked by a steady increase of complexity and increased automation. Contingently through the capital costs, an economic production is to be realized mainly through a high year utilization rate, reached by an aimed permanent supervision of the units in operation. Damages or unfavourable operating conditions have to be diagnosed and located at an early stage. The economic potential of increased availability is stated by a statistical evaluation of 1329 German capital goods industries in the year 1997 by the Institute of System Technique and Innovation. As a result only 50% of them reach a technical availability above 50%, only 5% reach values higher than 98% and the same amount less than 50%. Tofixthe initiating point of investigation a systematical evaluation of industrial life cycle potentials has to be done as by BMW for a car production line, which is characterized by: O the unique investment costs of 25% (planing, design, building-up, start-up, decommissioning), O the planned operation costs of 44% (maintenance, operation, energy, personal), and O the unplanned consecutive costs of about 31% (delays in start-up, unplanned reconditioning, faults). 169
As drawn out here the highest amount of saving can be seen at the unplanned consecutive costs by increasing the system's availability. Due to the interaction of several machine units in modem production lines, this problem becomes quite complex, including start-up, re-start, and normal as well as faulty operation with increased wear. The state of art of investigations for early failure detection emphasizes mainly on single system components of industrial machines, e.g. rolling element bearings, meshing gears, using vibration and acceleration analysis techniques. Actually extensive research work is done on the field of diagnostics of process data and machine control. Using this basic knowledge, actually, the global consideration of process lines has to be looked at, leading to new ways in the field of transient vibration signal analysis. Machine condition, machine faults and on-going damage can be identified in operating machines by fault symptoms, e.g. mechanical vibration, air borne noise, and changes in the process parameters like temperatures and efficiency. DATA ACQUISITION AND PROCESSING To the fulfihnent of the demands on comprehensive vibration analysis, an aimed instrumentation of the unit to be supervised is required whereby displacement, velocity and acceleration pick-ups are used. State of technology in vibration monitoring of rotating machines is related to the calculation of standard deviation and/or maximum values, their comparison with thresholds and their trend behaviour to determine increased wear or changes of the operation conditions. Spectrum analysis with special phase constant averaging routines allows to determine machine specific signatures by magnitude and phase relation. By correlation analysis common information of different vibration signals is evaluated for source localization, S' while cepstrum and hocerence analysis is used to quantify '^ ° °^'4l periodical information of spectral and coherence data.
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The global structure of a generally used monitoring system can be divided in three main parts, the data collection with data reports in digital manner, followed by the acquisition phase calculating the statistical values and functions in time and frequency domain with integrated data 1001J reduction to get fault and operational patterns. The more 5 4 difficult third phase of fault diagnosis is still under development and permanently adapted to the necessities of industrial applications, still mainly dependent on the acting personnel at the monitoring system. Investigations at several test benches and complex machines in operation are concentrating on the determination and separation of fault ^ and operation descriptive values in time and frequency domain. Different faulty operation conditions can show ^,^ ^J' similar changes of statistical vibration values as well as shapes of spectral signatures. Especially for complex machine arrangements with multiple step gears, several rollfrequency [kHz] ing element bearings, it is quite difficult to separate clear- Figure 1: Phase Enhanced Data Processing ly the different speed related excitations from structure or fluid induced components. A common method to identify speed related information is time averaging of the vibration signals using external signal triggering (e.g. by strobe light, laser, decoder). If those trigger units 170
are not installed, in case of multi shaft arrangements, or even for just in time measurements phase-enhanced data processing is applied as shown infigure1 at the example of source localization at the complex rolling element bearing arrangement of a fresh air fan. The amplitude spectrum of a non-averaged time signal includes the amplitude information of all frequency components, while their phase relation is lost. Fault specific patterns as the teeth mesh frequency with modulation, the blade rotation sound, as well as rolling element bearing frequencies are characterized by constant phase relations. Choosing one of these machine specific components as reference, calculating all phase relations relative to this component, the new calculated ''phase enhanced spectrum'' only includes the related phase constantfrequencycomponents, while the others will be damped by the averaging. The upper diagram in figure 1 shows the amplitude spectrum of acceleration measured at the casing of the fresh air fan. The averaged spectrum includes many narrow banded speed related information as well as broad banded system related excitations. To separate all these information becomes quite difficuh. If phase enhanced spectrum calculation is applied using the blade rotation sound (numbers of blades x speed = BRS) all driving speed related information is obtained, just as would be seen by time averaging. Applied here, the signal ground level drops down, while also the narrow banded speed related modulations are reduced, hinting to other main excitation sources than pure speed related ones (upper part spectrum). If one of the bearing characteristic frequencies is chosen, e.g. the ball pass frequency outer race (BPFO) of the cylindrical bearing NU348 similar results are obtained (middle part spectrum). Selecting the BPFO of the rolling element bearing FAG7252B proves it as nearly main excitation source by receiving nearly the original shape of spectrum as above for the "normal" averaged one (lower part spectrum). Additional data reduction by calculating the corresponding cepstra allows to determine excitation patterns. Further acquisition techniques for parallel processing and visualization of multi-plane measurements were developed for failure separation and source localization. Wavelet analysis and neural networks are actually investigated concerning their integration to existing monitoring systems. SUPERIMPOSITION OF MECHANICAL MISALIGNMENT AND THERMAL UNBALANCE The extension of analysis and visualization tools using already installed vibration sensors, additionally implementing time and rotor synchronous multi plane data acquisition and processing, often fit to determine the influences of reconditioning and operational changes to the machine's condition, as demonstrated by the following example of vibration assignment at a 920 MW steam turbine. After reconditioning and changing the LP-parts the vibration measurement system showed increased levels of rotor and bearing vibration with additional load and time dependencies. Related to the requirements of electrical consumption the operation is characterized by a large number of shut-downs and start-ups during which the vibration amplitudes reaches values far above the limitsfixedby the VDI/ISO standards. Single plane balancing was able to reduce the maximum values locally, but the general vibrational behaviour was still poor. The axialfixingkeys of the combined radial/axial bearing had to be changed due to wear after half a year of operation, only. An additional installed multi-channel parallel processing system should determine the complex rotor behaviour, looking for source indications of the machine's extraordinary vibration behaviour. Figure 2 presents the relative rotor displacement and the absolute bearing vibration as orbit plots at each measurement plane for different load conditions. To visualize the dynamical rotor behaviour the kinetic wave paths are drawn as 3D-diagram with an imaginary rotor axis. Connecting the synchronous points of the wave paths the dynamical rotor displacement is obtained, which in combination with the rotor sweep give hints to alignment conditions. The parallel processing allows to describe the phase relation of the rotor excitation and the mechanical bearing response. Directly after start-up and at low loads the rotor displacement in all measurement planes shows elliptical kinetic wave paths, with nearly 90" compounding maximum values at HP, HP-IP, and IP-LPl bearing hinting to some alignment problems. Similar vibration signatures are
171
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obtained at the LP2 and GE bearing, emphasized by counter rotation of the rotor sweep. -^,^1 Increasing the active load the — 2o4smax i4.i| phase angle of the S^-values '" stays stable, while the elliptical kinetic wave paths within the first three bearings collapse with superimposed stochastical excitations in direction of S,^. In addition to alignment problems this signature hints to thermal related compulsive forces inside the first three bearings, which also influence the bearing vibration between the LPl-LP2-part. Significantly at the exciter bearing the shape, the phase angle, and the maximum amplitude change, as well as for the kinetic rotor wave path as for the orbit of bearing vibration hinting to internal thermal influences.
The sensibility and instability of the complex rotor system to small changes of operational -50 0 50 850 MW parameters become certainly hor. [Mm] 210MVAR visible comparing the directions of Stationary rotor dislocation Figure 2: Relative Rotor Displacement and Absolute Bearing Vibration during start-up, stationary load operation, and shut down. The rotor position inside the bearing is determined by the DC-values of the here used eddy current displacement pick-ups, exemplarily drawn in figure 3 for several start-ups, shut-downs, partial and full load operation. As made visible, the rotor inside the HPbearing moves with increased speed to therightside, in the HP-IP-bearing to the lefl-hand side, and inside the IP-LPl-bearing again to theright-handside proving alignment problems within the first three bearings. After five days of operation the stationary rotor displacement during shut-down dropped down to the same level as at start-up. Due to the fact that the absolute position of the rotor inside die bearings cannot be determined exactly using the DC-values of the displacement pick-ups, the measurement must be calibrated by fixing the zero position, e.g. the slow motion during turn drive with a pre-heated turbine. As marked in the figure after a standstill of two days the rotor in the IP-LPl bearing behaves slightly different than in cases of a short time standstill with only low temperature gradients of turbine parts and foundation. Measurements prove a temperature related tipping of the concrete key in axial rotor direction due to thermal radiation of the IP-part. Additional mechanical load is added to the here located turbine's fixing point due to handicapped axial expansion in direction of the HP-part as becomes visible comparing different rotor positions at low active load (250 MW) shortly after start-up and increased temperature conditions at nearly nominal load (850 MW). The misalignment between HP, IP, and LPl seems to increase. By tipping the HP-IP-bearing to reduce the misalignment in combination with increasing the gap of the radial keys at the IP-LPl-bearing the absolute vibration levels in the HP- and HP-IP-bearing dropped down. The sensitivity of the system against alignment changes becomes obvious by the reduction of the maximum LPl-LP2-bearing vibration from 55 to 16 |im at maximum load of 920 MW.
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The complex measurement rehorizontal view sults implicate several excitation sources which influence the actual condition of the machine. The statistical evaluation of vibration signatures and the correlation with related operational parameters like electrical power, exciter current, certain structure temperature conditions, or the steam parameters often hint to excitation sources, too. One example is presented in figure 4, where exemplarily the maximum values of rotor displacement and velocity at the turbine and generator bearings are drawn as function of exciter current. Near to the determination of load dependencies within the vertical view single measurement planes, inFigure 3: Static Rotor Dislocation cluding threshold comparison of the absolute vibration values to the standards, the parabolic behaviour of the maximum rotor displacement in the exciter bearing shows superimposed mechanical and thermal unbalance, proved by the corresponding absolute bearing vibration, and visible as change of the phase angles of the S„,ax-values. 150.0
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Figure 2: Model showing the Inputs and Outputs of the Value-based Maintenance Methodology Step 1 - Get top management backing It is vital to the success of the asset improvement process to get support from the top management team. It is important that even the most unenthusiastic of team members realise that the full potential of the methodology will only occur if they support the process. The election of an individual is required to have responsibility for the process. This team leader should be either the Chief Executive Officer, Managing Director or senior director of the top management team who has the authority to guide and control the process in the firm. It is vital that the terms of reference are stated for the asset improvement process. All team members, the project title, the scope, time frame and the output need to be clearly stated. Also, any plans for the implementation of deliverables.
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Step 2 - Get them to define their Vision, Mission and business objectives It is important that the top team is clear on the direction and future direction of the firm. In many cases the vision, mission and business objectives are produced as part of the marketing departments role. This publicity material may not be a true and honest reflection of the top team thinking. Therefore, this has to be discussed and unanimously agreed before moving onto the next step. Also, they all have to relate and show a natural progression. Once this has been completed the business objectives can be Unked to the businesses prioritised cost drivers. Examples of these include economies of scale, capacity utilisation and location. Step 3' Use the Porter Generic Value Chain model The completed model provides a business with the ability to break up their business activities into nine segmented primary and support activity areas. This provides the basic platform for the physical asset analysis. All segments of the business have physical assets, however, it is important to prioritise segments to gain maximum benefit from the asset improvement process. In addition, it important to consider the possible and potential maintenance requirements in each segment. This is achieved by stating and then combining the percentage operating costs in each segment and the percentage value of assets in each segment. This analysis as shown in Eqn. 1 only provides a rough breakdown of where to target maintenance resources. Proportioned = Size of Maintenance for Segment
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The actual activities of the firm in each of the nine main activity segments have to be stated. For example, the primary activity of operations may include component fabrication, assembly and testing. These are then used in step 4. Step 4 - State who is responsible in top team for each activity It is important to assign responsibility within the top team for each of the identified activities. This may cross-existing management boundaries, so, the team leader will have to decide any disputes amongst team members. It is important that an individual is directly responsible for each activity. This is carried out to provide a clear, undisputed authority to each activity. Step 5 - State how activity should be measured and produce base line The activities and those responsible for them have been defined. It is now possible to decide how the firm should measure the performance of each activity. These measures are specific to the unique identity of the business. It is extremely important that the data to provide this information is readily available and that no duplication of activity measures is undertaken.
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Having finished this step will mean that the firm has produced a fuU set of Key Performance Indices. An initial compilation of these performance measures will provide a base line for future analysis. It should be stated that a timeframe is present in all the Key Performance Indices that are stated as examples in Eqn. 2, 3 and 4. These equations have been adapted from Wireman (1998). Total number of orders completed on demand Total number of orders requested Maintenance labour hours on emergency jobs Total maintenance labour hours Sales volume achieved to new customers Total sales volume
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Step 6' Decide the business challenges Only at this point will the firm be able to decide the new business challenges they face. Using the defined information from the previous five steps, the top team can produce a list of the business challenges that relate to each activity whether supporting or primary activity. The list of challenges produced has again any duplication removed. It is important that time is spent producing a detailed Ust and once completed should be compared to thefirmsstated objectives in step 2. Step 7 - Map the asset improvement challenges to the business challenges Using a structured matrix approach the top team can learn the asset improvement challenges they face. This set of challenges is directly resolved from the business challenges. The example shown in Figure 3 provides a possible example map for the activity of assembly. Business Challenges
Asset Performance Challenges Waste detection systems
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Step 8 - Decide which of the maintenance tools and techniques are going to be used This is the point when the tools and techniques of maintenance can be discussed and shown how to be used and delivered. The ten tools and techniques that the author has categorised maintenance into from information supplied in Kelly (1997) and Wilson (1999) are: > > > > > > > > > >
Organisation and Strategy Condition Based Maintenance Safety & Environment Objectives Review of Equipment Maintenance Training & Team working Systems & Data Management Maintenance Planning Stores & Spares Management Life Cycle Costing
SUMMARY OF COMPLETE SOLUTION The methodology that the author has called value-based maintenance has been integrated into a complete consultancy solution for the manufacturing business sector. This provides a complete and continuous asset performance improvement process. This business product is titled ADV@NCE™ (see acknowledgements). Stage 1 and 2 are the parts that form the focus of this paper. They provide the requirements for the third stage, that of creating the vision for asset improvement in the business. Further, Stage 6 uses the Key Performance Indices that have been defined. The following diagram represents the complete creating the vision and asset performance improvement plan. Stage 1. Top Level Strategy Stage 2. Mapping Business Challenges to Asset Performance Challenges
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CONCLUSIONS The value-based maintenance methodology outlined in this paper provides a clear structured decision making platform for a business that undertakes an asset improvement process. Understanding the impact of using maintenance in an enterprise can be achieved by measuring and analysing the activity based Key Performance Indices. This information can be used from the board level to technicians as well as supporting the sharing of the business requirements throughout all levels of a firm's hierarchy. The integration of an asset improvement consultancy solution with the value-based maintenance methodology provides a business with the ability to care, with the optimum efficiency for their physical assets, in a complete and continuous business process. Using the methodology, costs over time are attributed and not overlooked or ignored in all aspects of the business. This allows for the true benefits of applying maintenance for the business to be made visible, clear and understood. Finally, the approach is currently being used and developed within a selection of UK companies with the goal to investigate the link between business decisions and asset management decisions. Potentially, a computer based business solution will be offered to clients. REFERENCES Kelly A. (1997). Maintenance Strategy Butterworth-Heinemann, Oxford, UK.
Business-centred Maintenance,
Mitchell J. S. (1996). Beyond Maintenance to Value Driven Asset Management, Proceedings of the 5^ Int. Conf. on Profitable Condition Monitoring, December 1996, 37 - 43. Porter M. E. (1985). Competitive Advantage: Creating and sustaining superior performance, The Free Press, New York. Wilson A. (1999). (Ed.). Asset Maintenance Management - A Guide to Developing Strategy and Improving Performance, Conference Communications, Surrey, UK. Wireman T. (1998). Developing Performance Indicators for Managing Maintenance, Industrial Press, Inc., New York. ACKNOWLEDGEMENTS The author would like to thank the support and advice of Wolfson Maintenance in the production of this paper. ADV@NCE™ is a trademark of Wolfson Maintenance and is used by kind permission.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
VIBRATION-BASED MAINTENANCE COSTS, POTENTIAL SAVINGS AND BENEFITS: A CASE STUDY Basim Al-Najjar and Imad Alsyouf Department of Terotechnology - Vaxjo University, Vejdes plats 4, 351 95 Vaxjo, Sweden
[email protected] ABSTRACT Maintenance related expenses have usually been divided into direct and indirect costs. In this paper, maintenance related costs are identified and reclassified to reveal maintenance cost factors and highlight its profit. Maintenance profit is defined as the difference between the minimum savings resulted due to performing maintenance tasks during operational planned stoppages and investments in maintenance. The economic losses accumulated due to maintenance impact on production, quality, etc. are expressed as potential savings that could be recovered when a more effective maintenance policy is used. A model for identifying, monitoring and improving the economic impact of using vibration-based maintenance (VBM) is developed. This model provides additional possibility for identifying where, why and how much capital should be invested in maintenance. Further, the model is utilised to develop and use relevant maintenance performance measures, for monitoring cost factors and detecting deviations. The main results achieved, when the model is tested in a Swedish paper mill during 1997-2000; the average yearly maintenance profit achieved by using VBM was at least 3,58 Millions Swedish Krona (MSEK), and the average potential savings was 30 MSEK. It was not difficult to identify problem areas and know where investments should be allocated to eliminate the basic reasons and increase savings. The major conclusion is the better data coverage and quality the more control of maintenance direct costs, savings and more profits in maintenance. Also, it would be easier and more reliable to detect deviations in the maintenance performance and eliminate their causes at an early stage. KEYWORDS Vibration-based Maintenance, Maintenance Costs, maintenance Profits, Economic Losses, Potential Savings and Total Quality Maintenance (TQMain). INTRODUCTION Maintenance-related costs are usually divided into direct and indirect costs without considering maintenance savings and profits, which in turn implies falsely that it is no more than a cost-centre. Economic benefits that can be gained by more efficient maintenance can be found as savings in the results of other activities. But, the company's budget shows only direct maintenance costs, which constitute part of the operating budget. Mckone and Weiss (1998) cited that the amount of money du 217
Pont spent, in 1991, company-wide on maintenance was roughly equal to its net income. The total indirect maintenance costs such as loss of income due to breakdown stoppages and poor quality is, in many cases, not easy to estimate. In 1991, the direct and indirect Swedish maintenance-related losses were estimated to be about SEK 150-160 billion, where, in most cases the total losses that arise because of maintenance omission or ineffectiveness exceeds the purchase price of the equipment, cited by AlNajjar (1997). About 15-40% (28% on average) of the total costs for the manufactured products can be related to maintenance activities, ibid. The total utilisation of the equipment in Swedish industry is estimated on average about 55-60%, see Ljungberg (1998). So, industry could increase its production capacity without investing in new machinery if it implements an efficient maintenance policy. The most popular maintenance techniques are failure-based maintenance, preventive maintenance, condition-based maintenance, e.g. vibration-based maintenance (VBM), reliability-centred maintenance (RCM) and total productive maintenance (TPM). Nowadays based on experience and using the most efficient maintenance approach, failure can be reduced to approximately zero and the planned maintenance stoppages can be reduced as well by making use of better quality data, Al-Najjar (1997). VBM is becoming more widespread especially when the downtime costs are high. It gives tremendous possibilities to receive indications of changes of the condition of the machine in an early stage, Collacott (1977) and Al-Najjar (1997). These indications can be of great importance also in detecting deviations in the product quality early and before they show on quality control charts, Al-Najjar (1996, 1997, and 2001). The precision of the assessment of the condition of the machine and what actions have to be taken depends upon the technical efficiency and the precision of the condition-based system, Al-Najjar (1998 and 2000A). Better precision means fewer stoppages and lower production looses, i.e. lower costs and higher profits. Life Cycle Cost (LCC) has been widely used at acquisition of the most effective assets in the long term. In this study, we focus more on identifying the cost factors in LCC and on describing their behaviour during equipment life, so that it will be possible to monitor different cost factors and identify problem areas in the process to improve company's profits. Economic benefits gained due to improvements in VBM performance can be found in a wide range of plant activities and disciplines such as production, quality, assurance, and logistics, but it is difficult to specify maintenance impact on these activities. This is, among other reasons, why maintenance is counted as a cost- and not profit-centre especially when maintenance demands investments such as the case with using VBM.
MAINTENANCE COSTS AND POTENTIAL SAVINGS In this study, direct maintenance costs consist of the internal capacity costs needed for the maintenance function to perform its stated objectives such as direct labor, i.e. manpower, direct materials, e.g. spare parts, and overheads such as tools, instruments, training and other expenses. In addition to the external capacity offered by the original equipment manufacturers or others, i.e. outsourcing. Indirect costs are all the costs that may arise due to the planned and unplanned maintenance actions, e.g. lost production during stoppages. Usually it is difficult to estimate all these cost but nowadays with the assistance of the company wide IT systems much of the information needed for this purpose can be found. In general, the majority of these indirect costs (see below) are due to failures and short stoppages as a result of maintenance performance deficiencies, Al-Najjar (2000B): 1. Unavailability cost due to failure, and unplanned-but-before-failures-replacements (UPBFR). 2. Performance inefficiency costs due to idling minor stoppages and reduced speed. 3. Bad quality costs due to maintenance deficiency. 4. Idle fixed cost resources such as idle machines and idle workers costs during breakdowns. 5. Delivery time penalty costs due to unplanned downtime. 6. Warranty claims from dissatisfied customers. Compensation for product liabilities and repair. 7. Customer dissatisfaction costs due to bad quality and reliability, delivery delay or other reasons. 8. Extra energy cost due to disproportional energy consumption. Rao (1993) states that savings of up to 20% on the tremendous bill of the energy consumption in UK could be achieved by employing efficient monitoring and management strategies. 218
9. Accelerated wear due to poor maintenance. It was reported that loss due to corrosion of plants and machinery in UK, USA and other countries are of the order of 3 to 4% of GNP, Rao (1993). 10. Excessive, spare parts, buffer and work-in-progress (WIP) inventory costs. 11. Unnecessarily equipment redundancy costs to avoid waiting time after equipment failure. 12. Extra investments needed to preserve WIP and redundancies in good conditions. 13. Extra costs due to the absence of the professional labour as a result of maintenance-based accidents such as compensation labour costs and costs of using less skilled labour. 14. Environmental and pollution fines. 15. Extra insurance premium costs due to the increased number of accident and its consequences. The importance of these costs may be different for different companies, but they should all be considered when evaluating the maintenance role because they are representing the potential savings.
ASSESSMENT OF SAVINGS DUE TO MAINTENANCE LCC are divided into acquisition cost (AC), operating cost (OC), support cost (SC), unavailability cost (UC), indirect losses (IL), modification cost (MC) and termination cost (TC), see Eqn. 1. LCC = AC + OC + SC + UC + IL + MC + TC
(1)
Considering LCC factors, it is not difficult to realise that they are influenced by maintenance, which in its turn includes several cost factors. Some of maintenance cost factors such as labour and spare part costs can directly be related to maintenance activities. The other indirect cost factors such as maintenance related rejected items, maintenance related losses in market share and reputation, in many cases, hardly to be found in the accountancy system without being confused with other costs. Based on the available databases, not all the indirect cost factors such as losses of production due failures and UPBFR can easily be related to maintenance. To evaluate the economic importance of a particular investment in maintenance, it often demands the assessment of life cycle income (LCI). One way to do it is to assess the savings achieved through: 1. Reducing the downtime due to failures, UPBFR, planned replacements and repair. 2. Reducing the number of rejected items due to lack of maintenance/service. 3. Reducing inventory' capital, e.g. reducing redundancies of spare parts, equipment and personnel. 4. Reducing operating cost, i.e. reducing stand-by equipment and personnel when a high confidence in the used maintenance policy is created. 5. Less assurance premium due to less failure related accidents. 6. Less delay and more accurate delivery schedules. It can be approached by improving machine reliability and overall equipment effectiveness (OEE), i.e. availability, performance efficiency and quality rate, through using an efficient and continuously improved maintenance policy to detect deviations (and eliminate causes) in the machine condition in an early stage. The assessment of savings achieved by more efficient maintenance is easier than to assess LCI, due to the effect of the external factors (see below), e.g. on profit margin and product price: 1. Currency value in the international market is not usually stable rather varies. 2. World wide political crises and wars influence the cost of raw material, machines, etc. 3. New discoveries and products and new competitors. 4. New national or international regulations, e.g. those are related to environment. Talking, only, about maintenance direct and indirect costs is the first step to emphasise the claim that maintenance is more or less a cost centre. During recession, in general, companies cut down maintenance (cost) budget regardless of its generated benefits that have been collected by production, quality, safety, environment, etc. The usual and unrealistic question being asked by chefs is "why are we paying that much for maintenance while the plant does not suffer of so many failures and production disturbances?" without realising the role of more efficient maintenance in achieving these results. In this paper we attempt to introduce a new view of reality in order to place maintenance in its right position among plant activities, see Figure. 1. 219
Fig.l. Maintenance role in reducing production cost and increasing plant profit. TECHNICAL AND ECONOMIC EFFECTIVENESS Plant value-adding activities are usually monitored by technical measures such as overall equipment effectiveness (GEE). Usually the development of GEE and its elements are observed. But when it is considered in conjunction with the total production cost or with plant profits, it gives an impression of how much could the company reduce production cost and still satisfying customers, stakeholders and society in order to increase its sales and market share. To survive the hard competition, companies need to improve their manufacturing processes and profitability continuously. Continuous improvement demands effective tools for measuring and analysing data, results presentation, optimisation (and suboptimisation) and reliable decision-making procedures. In general, quality rate is influenced by many factors, some of them related to the machine design and construction, raw material, cutting tools, environment, quality control system, company culture, etc. The others arise because of the selected maintenance policy, service and maintenance performance quality. In many cases, especially when there are long-term (chronicle) problems, the reasons behind quality problems are particular combinations of some of the above mentioned factors. This means that high quality input elements needed for establishing a manufacturing process should be maintained in order to secure high quality product at a competitive price through high availability and stable product quality (quality variation within narrow limits). These can not be secured without an effective maintenance policy, Al-Najjar (2001 A). Such a policy will be very useful to reduce short stoppages and enhance performance efficiency as well. Less failures and better control of the production plant help minimise pollution generated and fulfil society's demands. In general, these results can be achieved in the following steps: 1. First step; when the customers are satisfied, 2. Second step; the society will gradually (and widely) accept the company and its products, and 3. Third step; stake value increases, which makes stakeholders satisfied and stake-demand increases, which leads in its turn to an additional increase in stake value. Note that along all these steps profit is generated, because the more the customers are satisfied and the company (and its products) being accepted by the society, and the stake value increases the more profit can the company gain, and vice versa. 220
MODEL DESCRIPTION The suggested conceptual model includes five main parts that are shown Figure 2: the maintenance related cost factors (potential savings), the direct maintenance cost, maintenance savings, maintenance profits, and maintenance performance measures. At first, direct maintenance cost and the related maintenance costs factors should be identified (which may not necessarily be the same for different companies). The next very important essential step is to know where to find the required input economic and technical data in the available accountancy system/database and how to calculate or estimate them. Then, the data can be used to calculate/estimate the maintenance related potential savings, i.e. productions losses. The minimum savings that have been achieved by more efficient VBM policy, which is based on utilising operational planned stoppages to perform the necessary maintenance tasks, can be assessed as well. Knowing the maintenance investments, which is part of the direct maintenance cost, and the minimum savings achieved by better maintenance planning enable the user to estimate maintenance profits. Further, based on the maintenance cost factors (potential savings) and other relevant information parameters maintenance performance measures can be identified and calculated. Trends, i.e. rate of change, of the interested maintenance cost factors and performance measures can then easily be obtained from the available data in the model, i.e. past and current data, for the study period. Furthermore, using analysis tools such as Pareto diagram help the decision-maker to identify the problem areas and perform the never-ending improvement process, i.e. KAIZEN, which is based on Deming; cycle Plan-Do-Check-Act. This can be based on doing technical analysis to relate problems and their causes to the expected results that might be achieved by means of new investment, i.e. to describe which economic losses can be eliminated through performing particular improvements in the maintenance policy. For example if the maintenance personnel get more reliable support such as using a new monitoring and diagnosis system that eases detecting and localising of damages, then fewer failures and UPBFR should be expected. Comparison between the expected and achieved results is a reliable indication to check whether the investment was cost-effective or not. This will enable the user to act and localise where the next investment should be, and so on. Where to find the data in the Plant Databases?
Equations to calculate the model's factors.
;;=
2) Direct Maintenance cost (inclusive invetments)
[^^ 3) Savings that could be achieved due to more effective maintenance policy
4) Maintenance profits
Fig.2. Maintenance cost, savings and profits model. In order to assess the savings achieved due to using more efficient maintenance we consider that all the maintenance tasks such as condition-based replacements (CBR) being performed during operational planned stoppages, which are scheduled by production department for doing some other tasks, are avoided failures. CBR, e.g. bearing replacements, are recommended by VBM after detecting damage. When these replacements are done in parallel during planned stoppages to avoid failures and utilise the planned stoppage time. Therefore, all the corresponding costs, i.e. profits losses and unutilised fixed cost expenses during the performed CBR tasks, could be counted as savings. These are considered the 221
minimum savings because if the replaced components failed during the planned operating time, they will probably occur one at a time and the time needed to do the task will be longer with more failure consequences. In general, the direct maintenance cost is required to maintain machine quality and fulfil production requirements. But, deterioration in the essential elements of the machinery is not avoidable forever even if it is possible to be arrested for a while. Therefore, more production losses can be generated, which motivate special investments for reducing these losses. In this case, the reduction in the losses is called saving, i.e. maintenance related recoverable expenses. It equals to the difference in the losses (potential savings) of two following periods if no other investments such as those in quality, production procedure and instructions (in addition to that done in maintenance) have influenced some or all the savings. As long as it is possible to reduce the economic losses by reducing failures, short and planned stoppages and decreasing stoppage times through improving repair, these economic losses, potential savings, i.e. these are recoverable expenses. This means that by means of more efficient maintenance policy some of these production losses can be recovered. The model presented Figure 2 can be utilised to achieve: 1. Keep track of the minimum savings being generated by maintenance due to new investments for improving maintenance performance, which is not possible otherwise. 2. When the minimum savings are classified according to the basic cost and potential saving factors, it will be easier to identify where, how much and why a new investment should be done. 3. The achieved savings can be compared with the investments done for improving maintenance policy to reveal whether the investment was cost effective or not. 4. Develop and use relevant performance measures utilising the available data to detect deviations before changes become unacceptable. 5. Achieve the never-ending improvement process, i.e. KAIZEN. The new in this model can be summarised in the following: 1. Monitoring changes in the relevant maintenance related cost and potential saving factors, which can be identified in (or estimated from) the existing accountancy system of most companies. 2. Direct maintenance cost can not be reduced to zero even if all the losses are eliminated. These costs are required to keep the condition and quality of machinery high enough to fulfil company's requirements. But, the investments in maintenance are for improving maintenance and overall company performance and reducing economic losses. Therefore, the minimum savings would be considered to justify these investments. 3. Using the model, maintenance would be considered as a profit-centre instead of cost-centre as long as the minimum savings can be identified and monitored versus investments. CASE STUDY The case study was conducted at StoraEnso (a paper mill company in Hyltebruk area in the southern part of Sweden). The data collected was delimited to only stoppages of mechanical components, which were (or could be) monitored by vibration signals. The study was conducted at PM2, one of the company's four machines. It was selected due to its valuable database especially during study period (1997-2000). A special data sheet was designed for collecting, manually, technical and economic information parameters from the company databases. The data sheet was adapted to suit the company terminology and context. Technical data included parameters such as planned operating time, planned production rate, time and frequency of planned stoppage in which mechanical tasks, e.g. bearing replacements, were performed as a result of using VBM, unplanned stoppage, i.e. failures and UPBFR, short stoppage, quantity of bad quality products caused by maintenance problems. Economic data such as fixed and variable operating costs, profit margin, net profit, working capital, direct maintenance costs, investments in maintenance, spare parts inventory, etc. were also collected. The conceptual model was validated using the data collected from the case company. The first factor is direct maintenance cost which was almost constant during the study period with an average of about 13 MSEK, see Figure.3. The total maintenance investment in PM2 both in general and training was increasing for the years 1997-1999 with a little bit decrease in year 2000, as shown in Figure 4, on average it was about 0,455 MSEK per 222
year. The total production losses (potential savings) consists of the summation of profit and unutilised costs calculated for unavailability due to failures, UPBFR, and planned stoppage times; short stoppages; bad quality products caused by maintenance problems; and tied up capital due to extra spare parts inventory. On average the total potential saving w^as about 30 MSEK, and it was increasing, as shown in Figure 5. Pareto diagram for the total losses elements are shown in Figure 6. We can see that losses due to short stoppages represent the highest value, then the planned stoppages, and quality problems, after that comes the failures and UPBFR, finally the tied capital due to extra spare parts inventory, which was calculated with respect to year 1997. On average the minimum savings was estimated to be about 4 MSEK, which was increasing especially during the years 1999 and 2000. The last factor is maintenance profit, which represents the difference between the minimum savings and maintenance investments. On average it was about 3,58 MSEK.
Maintenance Mechanical Dmct costs for PM2
mSBH
mUBtnimmm Ummmcm Direct C0SIS for PUZ
FigJ, Maintenance mechanical dircci costs Total maintenance Inveslments In PM2
X
OJO0 OJO0 0JOO 0.500 0,400 0,300 0.200 0J0O 0,000
vv ^v j-v-'-«ii«^«' ___^_^_^ p^*§^i?||!^§f^^^ -f.' -»»'• •«;**ii'*.• iw
0
50
':.^v:'--rr^ ' ^X"( ^ --,ir«" W^ %
100
150
y
200
250
N,.-l
300
VV\J/
]"f
350
Rotor Angle (degrees)
Figure 4: IAS at 20V Phase Imbalance Applying a further 20V drop on the phase, the IAS waveform fluctuates with a higher speed change amplitude (RMS=1.32), as shown in Figure 5. Therefore, severity assessments can be regulated according to those RMS values to classify the errors in the induction motor rig as healthy, abnormal, or faulty. Notably, the amplitude of modulation has increased as a result of further decrease in the strength of each magnetic pole.
315
Instantaneous Angular Speed of 40V Ptiase Imbalance at 0% Load
1 An^Mliicle of modulation haB Nicraased stM fiuither.
-yr^
[y^
Y
pTTl-
A
"I
—7 *^'\
v^
100
150 200 Rotor An(^a(
=vy'',K2=K2e~""'
(3)
l,=l,e"'^-,'l,^^l,e'""'
(4)
r„=r„.^'"',r>.=v^,2^"^"
(5)
Substitution of Eqns.2-5 into Eqn.l yields: t'
average
pulsating
^ ^
where T^^^^^^^^ is the electromagnetic torque, which is constant in time and contains two components (the positive and negative torque sequences - T, and Tj respectively) ^average
330
~ ^\
~ ^2
V' )
where T, =(3P/26;i)(|F,,||/,,|cosa, -|7,,f ^.,)
(8)
T^ =:(3P/2a>,){|F,.2||7,2|cosa,2 -|7,2|'/?,)
(10)
The most important feature obtained from Eqn.6 is the pulsating torque component r^„/va/,»K which can be expressed as follows: 7'M™.« =(3^/2«,)Re[(F„7,, -Vjje'"""}
(11)
It can be seen that this torque component pulsates at an angular frequency of 2 ^,. The pulsating torque is caused by both the interaction of the positive sequence stator current and negative sequence stator flux linkages and also by the interaction of the negative sequence stator currents and positive sequence stator flux linkages. Therefore, the 2co^ double frequency time varying pulsating torque component is the symptom of an asymmetrical stator fault. This frequency does not exist when an induction motor operates under symmetrical stator conditions.
2.2 Induction Motor Operation with Asymmetric Rotor Faults Similarly, the effects of asymmetric rotor faults (for example, due to unbalanced internal resistance through a broken rotor bar) can be deduced. In the steady state and by referring to Eqns. 1 -5 7:,=|p(^,.x7,.)
(12)
The electromagnetic torque T^ under asymmetrical rotor conditions is gained as follows: f
average
pulsating
^
•'
where L.e.ag. = -
^
2sco^
f
J | 7 , , | c O S a , , -j7^,|'i^^. - | F , , | | 7 , , | c O S a , , - | 7 , , f 7 ? J I
II
I
i
I
I
II
T^,....,=j^R4{VA^-vJ,y'''''']
1
(14)
I I
(15)
From Eqn.15, it can be seen that the biggest difference with Eqn.l 1 is that the frequency of oscillation is now 2s0)^. The difference between Eqn.l 1 and Eqn. 15 is caused by the stator and rotor asymmetries respectively. Generally, the symmetrical stator windings produce a rotating field at frequency co^ when symmetrical supply currents are applied to the stator windings. This rotating field induces an electromagnetic force in the rotor bars at frequency sco^. Since the rotor bars (with end rings) can be considered as phase windings of the rotor, the induced alternating currents will flow through the rotor bars. Thus the rotor phase windings with the induced alternating currents of the rotor will produce only a positive sequence fundamental component of mmfs of the rotor (which rotate at frequency sco^ if the rotor is symmetrical). If a bar is broken or if there is some other rotor fault, an asymmetry is created.
331
The induced currents of the rotor will then cause a negative as well as positive sequence component in the stator windings because the resultant negative sequence component is no longer zero. The angular speed of the positive and negative sequence mmfs are co^+sco^ = co^ and co,.-sco^ = (1 - 2s)o)^ with respect to the stator. Therefore the angular frequencies of the positive and negative sequence stator currents are co^ and (2^ -1)6>, respectively. It can be seen that the stator currents now consist of the normal supply frequency component o)^, together with a component (1 - 2s)cO]. The variable frequency component has the effect of modulating the supply frequency component at twice the slip frequency. Therefore, the 2so)^ frequency pulsating torque and its harmonic components are the symptoms of an asymmetrical rotor fault. These frequencies do not exist when an induction motor operates under symmetrical rotor conditions.
3. EXPERIMENTAL RESULTS 3J Vibration Analysis for Induction Motor Faults An induction motor test facility was designed, built and refmed. The test rig consists of a 3 kW induction motor, a 5 kW DC motor used to absorb the power, a resistor bank used for putting load on the motor under test and corresponding control boxes and instrumentation. The instrumentation consists of accelerometers, Hall effect current transducers, a high resolution speed encoder (360 pulses per cycle), amplifiers, filters and a DIFA 210 data acquisition system. Figures 1-4 present stator vibration spectra (on a logarithmic scale) under 0%, 25%, 50% and 75% operational loads. Comparison is made between a healthy induction motor and one with a broken rotor bar (that creates rotor asymmetry). For the asymmetrical rotor fault, the 2sco^ components are expected. In Figures 2(b)-4(b), illustrating results for the motor with a broken rotor bar and above 25% load, the sidebands can be seen around the fundamental rotor frequency. These frequency components are slip dependent and shift outwards from 0.6Hz, 1.60Hz, 2.34Hz and 3.12Hz when the loads are increased. When the motor is healthy, no sidebands are visible (even as loads increase from 0%)-75%). With the motor operating under no load (Figure 1), sidebands are not detectable in the vibration spectra whether a rotor bar is broken not. The reason for this is because of the slip being too small. As stated previously, asymmetrical stator faults (caused by stator winding faults or asymmetrical phase supply voltages) are also common in induction motors. An asymmetrical stator system fault was physically seeded in the test rig by decreasing one of the three phase supply voltages. Figure 5-6 shows the vibration spectra (on a logarithmic and a linear scale) from a test motor. A comparison is made between healthy operation and 20V and 40V drops in one phase of voltage supply (creating an stator asymmetry). As expected, the faulty characteristic frequency for this fault condition, 2co^ = 2 x 5 0 = 100Hz, increases when the asymmetrical stator content is introduced and increased. When the same spectra are displayed on a linear scale (Figure 6), the 100 Hz component is seen to substantially increased when an asymmetrical stator fault is introduced.
3,2 Stator Phase Current Analysis for Induction Motor Faults Figures 7-10 show phase current spectra under different load conditions for a healthy motor and for a motor with one broken rotor bar respectively. These zoom spectra are centred on the fundamental supply frequency (50Hz), with a frequency span of 100 Hz (+50 Hz and -50 Hz). It can be seen that very similar phenomena to that seen in vibration spectra occur in this phase-current spectra. The sideband locations are related to load and subsequently slip. The slip and sidebands (0.6 Hz, 1.6 Hz,
332
2.34 Hz and 4.6 Hz) are associated with a range of loads (0%, 25%, 50% and 100%). As with the vibration spectra, when the loads are below 25%, the sidebands (due to the motor with a broken rotor bar) are not visible because of the small amount of slip. When the loads are increased above 25%, a series of sidebands appear in the expected locations. These sidebands in the phase current spectra are much clearer than those in the vibration spectra. Figures 11-13 show the three-phase current spectra when Phase-A was forced to drop by OV, 20V and 40V from the original phase voltage. It can be seen that the current spectra gradually show sideband presence around the harmonics of supply frequency (indicating stator current modulation) as the extent of asymmetry is increased. However the variation of the two times supply frequency (at 100 Hz) is hardly visible in phase current spectra. This is expected because the dominant odd harmonics and the arrangement of stator windings are contributed to minimise the even harmonics in phase currents. This effect does not affect the sensitivity of phase current analysis for detection of asymmetrical supply faults as this fault can be easily detected by observing the difference between the three-phase stator currents.
3J Transient Speed Analysis for Induction Motor Faults From theoretical analysis, it is known that for asymmetric stator or rotor faults, the corresponding fault symptoms are sidebands spaced at ISCD^ and 2o)^. These components give rise to torque ripples at frequencies of 2sco^ and 2co^ respectively, which produce speed ripples at differing amplitudes. Therefore monitoring rotor speed fluctuations via a high resolution encoder and performing additional signal processing also provides information on the motor condition. Figure 14 presents the speed variation of a healthy motor during start-up. After the speed reaches a steady state condition the speed curve is reasonably flat. No fluctuations of speed caused by 2sco^ and 2co^ (100 Hz) can be seen and this is further proved by a corresponding spectrum (Figure 18 (a)). This indicates that the motor is running under normal, healthy conditions. Figure 15 shows the speed curves for the motor with one broken rotor bar and two broken rotor bars. It can be seen that the average speed decreases when the number of broken rotor bars is increased. The speed spectrum in Figure 18 (b) indicates that there is a very small 2sco^ (4.6 Hz) component and the 2co^ (lOOHz) component slightly increases compared with speed spectrum for a healthy motor. Figures 16-17 illustrate the speed variation caused by an asymmetric stator fault. A large speed ripple can be observed at 2ty, = lOOHz (Figure 18 (c) and (d)). This suggests that the pulsing torque caused by the asymmetrical supply voltages give rise to speed fluctuations. When the extent of asymmetry in the stator increases, the speed ripple increases proportionally. This is clearly seen in both the speed time curve and spectrum. However the speed spectrum of a motor with a broken rotor bar (Figure 18 (b)) does not show any sidebands around the fundamental rotor speed. This is because the expected speed ripple modulation caused by the broken rotor bar fault could be absorbed by the inertia and load of the rotor [5].
4. CONCLUSIONS Three methods (vibration, per-phase current and transient speed analysis) have all been assessed on their ability to detect induction motor faults. It was found that they all possess their own advantages and disadvantages. Vibration analysis was found to be sensitive to both asymmetrical rotor and stator faults. However the main drawback of this approach was the requirement for detailed information on motor design characteristics such as knowledge on the frequency response functions (FRFs). FRFs were required because mechanical and electrical responses will vary at different accelerometer positions making quantification of fault conditions. Per-phase current analysis was found to be very sensitive to asymmetrical rotor faults (such as broken rotor bars). However, because of the design of induction motors, the variation of the 2x frequency 100 Hz is difficult to detect in phase current spectra. This does not affect the sensitivity of the per-phase current analysis for detection of 333
asymmetrical stator supply faults as this fault can be easily detected by observing the difference between the three-phase stator currents. Transient speed analysis provides a good indication of asymmetric stator supply because the faulty pulsing torque created by the asymmetric stator and rotor faults causes speed ripples at frequencies of 2sco^ and Ico^. These components can easily be detected when compared with the speed spectra for healthy motor operation. However the sensitivity of the technique depends on the external load and rotor inertia as these influences are capable of filtering out the symptomatic speed modulation around the fundamental rotor speed.
REFERENCES [1] Robert R. G. (1984). "Computer techniques applied to the routine analysis of rundown vibration data for condition monitoring of turbine-alternators". Proceedings of International Conference on Condition Monitoring. Swansea. UK. 229-242. [2] Cameron J. R., Thompson W. T. and Row A. B. (1982). "Vibration and current monitoring for detection air gap eccentricity in large induction motors". Proceedings of International Conference on Electrical Machines. London. 173-179. [3] Thomson W.T. and Chalmers S.J. (1987). " An on-line, computer based current monitoring system for fault diagnosis in 3-phase induction motors". Proceedings of the Third Turbo-machinery Maintenance Congress. London. Vol. 1.1 687-693. [4] Vas, P. (1992). ''Electrical machines and drives, A space-vector theory approach'', Oxford University Press [5] Filippetti, F., Franceschini, G. and Tassoni, C. (1998). "AI techniques in induction machines diagnosis including the speed ripple effect", IEEE Transactions on Industry Applications, Vol.34, No.l
NOMENCLATURE I ^, I^
Stator & rotor phase currents
Ta.ara^.' Tpuisaun^ Average & pulsating torque
/ , , , /,2 Stator positive & negative sequence currents
T^
Electromagnetic torque Stator phase voltage
7^1, fj
Rotor positive & negative sequence currents
V^
P
Number of poles
^vi' ^v2 Stator positive & negative sequence voltages
R^
Stator resistance
R,.
Rotor resistance
S
Motor slip
F,.,, V,.2 Rotor positive & negative sequence voltages CO^, CO^ Rotor speed & Supply frequency if/^, ij/^. Stator & rotor flux linkages
Tj, 7^2 Positive & negative sequence torques
334
(a) A healthy motor under 0% load
,,i||iilW^^ Frequency(Hz) (b) A Motor with one broken rotor bar under 0% load
Frequency(Hz)
Figure 1 Vibration Spectra under 0% Operational Load (a) A healthy motor under 25% load
Figure 2 Vibration Spectra under 25% Operational Load
335
(a) A healthy motor under 50% load
Frequency(H2) (b) A motor with one broken rotor bar under 50% load
Frec|uency(Hz)
Figure 3 Vibration Spectra under 50% Operational Load (a) A healthy motor under 75% load
Frequency(Hz) (b) A motor with one broken rotor bar under 75% load
2.
Figure 4 Vibration Spectra under 75% Operational Load
336
(a) A healthy motor with symm^rical supply voltages
°- -100
80 Frequency(H2)
100
Figure 5 Vibration spectra for Asymmetrical stator faults (Logarithmic scale) (a) A healthy motor with symmetrical supply voltages
E S o
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r
-
1
100 Hz
.^J .
,
50 100 (b) One phase supply voltage with 20 volts drop /
50 100 (c) One phase supply voltage with 40 volts drop E
50
100 Frequency(H2)
Figure 6 Vibration spectra for Asymmetrical stator faults (Linear scale)
337
(a) A healthy motor under 0% load
40
50 60 Frequency(Hz)
(b) A motor with one broken rotor bar urKfer 0% load
40
50 60 Frequency(Hz)
Figure 7 Phase Current Spectra under 0% Operational Load (a) A healthy motor under 26% load
40
50 60 Frequency(Hz)
(b) A motor with one broken rotor bar under 25% load
Figure 8 Phase Current Spectra under 25% Operational Load
338
(a) A healthy motor under 5 0 % load
Wv^WuJiALAAL^
.ft/Vv\J 40
50 60 Frequency(H2)
(b) A motor with one broken rotor bar under 50% load
40
50 60 Frequency(Hz)
Figure 9 Phase Current Spectra under 50% Operational Load (a) A healthy motor under 100% load
Figure 10 Phase Current Spectra under 100% Operational Load
339
90
100
(a) A healthy motor with symmetrical voltages
50 100 0.8
1 *' rotor speed
0.6
1^-rotor speed
; 0.6 !• J
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0.4
100 Hz
4.6 Hz 0.2
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n JS[.
.
100 Hz A*""^ 100 200 Frequency (Hz)
OL
300
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Figure 18 The Spectra of Transient Rotor Speed with 100% Load
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
NEW METHODS FOR ESTIMATING THE EXCITATION FORCE OF ELECTRIC MOTORS IN OPERATION Hiroki OTA^ Taichi SATCP, Masayuki TAGUCHI^ Joji OKAMOTO*, Makoto NAGAI^ and Katsuaki NAGAHASHI^ 1 Applied Systems Engineering, Graduate School of Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama, Hiki-Gun, Saitama 350-0394, JAPAN ^Depgutment of Intelligent Mechanical Engineering, Tokyo Denki University, JAPAN ^Toppan Printing Company Limited, JAPAN ^Hitachi Air Conditioning Systems Company Limited, JAPAN
ABSTRACT We developed two methods for accurate estimation of the excitation force of a motor. One is a hybrid method combining an experiment and afinite-elementanalysis. The other is a calculation method that uses both the equivalent circuit andfinite-elementmodel of a motor. The motor excitation force determined by the two methods was compared with the result obtained through direct measurements by using a load cell. The acceleration response of a thin plate excited by a motor was also calculated and the measured and calcdated accelerations of the thin plate were compared and analyzed in terms of accuracy. KEYWORDS Motor, excitation force, phase angle,finite-elementmodel, torque pulsation, vibration response INTRODUCTION Electric motors are used as actuators in many different machines. Vibratory motions and noise in machines arise when the excitation forces of their electric motors act on their elastic parts. An increasing demand for low-vibration and low-noise machines has accelerated the development of new methods for estimating the excitation forces of electric motors. Several studies have focused on vibration and noise caused by electric motors (Timar P. L. (1992), Craggs J. L. (1993), Matsubara K. et al (1996), and Noda S. et al (1996)), and a number of methods for measuring excitation forces have been developed (Mugikura (1989), Hiramatsu T. (1989), Yamagami H. (1994), and Sato T.etal (1999)). 345
The excitation force of an induction motor is composed of many peakfrequencycomponents. The highest peak of the force appears at afiiequencyof 100 Hz, which is twice the power-supply frequency. We called this frequency component a **2f component", and analyzed the force of this 2f component. When the motor is in operation, the torque of the motor fluctuates at twice the power-supply frequency. This torque pulsation is the cause of the excitation force of the 2f component. Based on the mechanism of excitation-force generation, we developed two methods for estimating the excitation force of a motor. One is a hybrid method that combines an experiment and a finite-element (F-E) analysis. In the experiment, the circumferential acceleration of a motor housing was measured when the motor was in operation. Additionally, a F-E model was created for the motor, and the circumferential acceleration response of this model was calculated. The measured and calculated accelerations were then used to estimate the motor excitation force. The results obtained by this hybrid method agreed well with the results of the excitation force measured by using a load cell. The other method is a calculation method. Using this method, the excitation force of the motor was estimatedfromboth the equivalent circuit and F-E model of the motor. To obtain the torque pulsation of the motor, we used a two-phase equivalent electric circuit corresponding to that of the motor. We then calculated the vibration response of the F-E model wiien the torque pulsation acted ontibemotor housing. Through this calculation process, the excitation force of the motor was estimated. We found that the calculation results were close to the measured ones. CHARACTERISTICS OF MOTOR EXCITATION FORCE This study investigates the excitation force of a motor as shown in Table 1. Figure 1 shows the frequency response of the force produced by the testing system. The testing system, based on the direct method, consisted of a motor, a block with high rigidity, and a load cell. A number of peakfrequencycomponents are shown in the figure. Among these peaks,tiiehighest peak is at 100 Hz. This highest peak of the force is afrequencycomponent that is twice the power-supplyfrequency.We called thisfrequencycomponent a "2f component" and analyzed the force of this 2f component. In this study, "motor excitation force" refers specifically to the force of the 2f component. The motor was supported by four feet Therefore, in addition to the amplitude of the excitation force, the phase angle of the force for each foot was also calculated. TABLE 1 MOTOR SPEOnCAnONS Induction motor Motor type 30W Rated power Power supply fi'eaueiicy 50 Hz Power supply voltace 200 V 6 Pole 24 Stator-slot 34 Rotor-slot 0.04 (»960 rpm; without load) Slip
HYBRID METHOD To simplify the measurements, we developed a hybrid method for estimating the motor excitation force without using a load cell. This method combines an experiment and a F-E analysis. In this hybrid method, an accelerometer was used instead of the load cell to estimate the excitation force of a motor. The circumferential acceleration. A, was measured by the accelerometer attached to the motor housing. A F-E model 346
A : Whirling motion B:2f component C : Slot ripple component
100
ISO
200
250
300
3S0
400
4S0
500
Frequency Hz
ra) Frequency response of the excitation force of motoi (b) Experimental device (direct method) Figure 1: Motor excitation force and experimental device
for the motor was also created as shown in Figure 2. The circumferential acceleration, A\ and the motor excitation force, F\ of this model were then calculated using the F-E program MSC/NASTRAN. If the actual motor excitation force is represented by F, then^* and F* have the following relationship F F_ A^ A
(1)
Because the values of ^, F\ andv4^ were determined by an experiment and FEM calculations, actual motor excitation force F can be obtained from Eqn. 1 as follows.
Footl:F,' Foot2:F
Foot4:F/
Figure 2: F-E model of the motor The calculation results of the motor excitation force and those obtained experimentally without using a load are compared in Figure 3(a). When the power-supply voltage increased to 100,140, and 200 V, the motor excitation force estimated by both the calculations and experiment also increased and the experimental results were in good agreement with the calculation results. At the same time, the phase angle of the motor excitation force was calculated for each supporting foot. The experimental results for the phase angle were in good agreement with those obtamed by the calculations as shown in Figure 3(b). 347
lOOV
140V
200V
(b) Phase angle of excitation force of 2f component for results each supporting foot
(a) Amplitudes of motor excitation force
Figure 3: Hybrid method
CALCULATION METHOD We developed another method for estimating the motor excitation force without direct measurements. It is based on using the equivalent electric circuit of a motor to obtain the torque pulsation. The torque pulsation acts on the motor housing as a reaction force, and the motor housing vibrates in the circumferential direction. If we estimate the circumferential acceleration of the motor housing as was described above, the motor excitation force can be calculated by using the F-E model of the motor. Torque pulsation The torque pulsation of the motor can be calculated by using a two-phase equivalent electric circuit (Morrill W. J. (1929)). The equivalent electric circuit is shown in Figure 4. The 2f component of the torque pulsation, Tv, can be expressed by the following equation: • {(«/ -^hih-^1]
^ fu\ + '''l^f + 2a|/;|x|7,|cos(0-^) (200 V)
I
^
1
I 0
500 Speed rpm
1000
Rgure 6: Speed-torque pulsation characteristics Calculation ofmotor excitation force The circumferential force acted on the motor housing as a reaction force of torque pulsation. The circumferential force contributed to the deformation of the end plate of the motor. We thus calculated the motor excitation forces generated at the motor supporting feet by using the F-E model shown in Figure 2. The calculation results of the motor excitation force and those obtained experimentally without a load are shown in Figure 7. When the power-supply voltage increased to 100,140, and 200 V, both the calculated and the experimentally measured motor excitation forces increased. The results for the phase angle were the same as those obtained using the hybrid method (Figure 3(b)), because of the use of the same F-E model in both the hybrid and calculation methods.
100 V
140 V
200V
Figure 7: Calculation results of the motor excitation force VIBRATION RESPONSE ANALYSIS Experimental device and F-E modeling We used the excitation force previously measured and calculated to analyze the vibration response of a thin plate structure. The thin plate structure was a 800*600*3 mm steel plate with a motor located in its center as shown in Figure 8. We created an F-E model for the experimental device (Sato T. et al (1999)). In this F-E model, the steel plate was composed of a shell element, and the motor was composed of a concentration mass and several beam elements. The motor excitation force was inputted in four places to be equivalent to the motor foot, and the vibration response was calculated. 350
Thin plate (800*600*3 mm) •~™
(gi
jp
(Qi
1~
700
Figure 8: Thin plate and motor Vibration response The acceleration response of the thin plate excited by the motor was determined by using the F-E model. The amplitudes and phase angles of the motor excitation force used in the vibration-response analysis were classified into three different categories depending on how the motor excitation force was obtained. Each of the three categories corresponded to the motor excitation force that was, respectively, (a) measured directly, (b) obtained by the hybrid method, and (c)calculated using the equivalent electric circuit (calculation method). Additionally, we assumed that the excitation force on each foot of the motor was in phase. Figure 9 shows the calculated and measured responses of the 2f component. The parallelogram represents 1/4 of the thin plate. The acceleration amplitudes at each point of the plate are shown in the bar charts. The gray bars represent the calculated responses, and the slanting^line bars represent the measured responses. In the direct measurements (Figure 9(a)), the calculation results agreed well with the experimental results for each point with the exception of a small area in the plate. In the hybrid method (Figure 9(b)), the calculation results represented the characteristics of the acceleration distribution quite well. However, the responses calculated for each point were a little smaller than the ones measured experimentally. The phase angle is the main cause of the difference between the calculated and measured results. In the calculation method (Figure 9(c)), it is clear that both the gray and slanting-line bars agree well with one another for each point. However, the responses calculated in phase as shown in Figure 9(d) were much larger than the measured ones. In otiier words, the vibration response largely depends on the phase angle. Therefore, we were able to confirm that an accurate calculation of the phase angle of the motor excitation force'\^important for an accurate calculation of the vibration response.
CONCLUSIONS We developed a hybrid method for estimating the motor excitation force that combines an experiment and a F-E analysis. We also developed a calculation method that uses an equivalent electrical circuit and an F-E model of a motor. Using these two methods, the vibration response of a thin plate structure was accurately estimated.
351
f
B Csldzlation
19 CMlculAtion
6
(b) Hybrid method
(a) Direct measurements
MCalculation HJbyrii—nt
?
r
O
Solenoid drain valve
»
Hand-operated drain valve
Sump tank
"•~T"'" Pump
Sump temperature
Figure 1: The main features of the processrigused as a basis for the Fieldbus system During normal operation, water stored in the sump tank is pumped through the piped circuit into the process tank for conditioning by an electrical water heater in the process tank, and then released back into the sump tank when the demanded conditions are reached. Solenoid valves are available for diverting the water through the cooler if required, and also to release the water from the process tank when appropriate. A number of hand operated valves are also available, which can be used to introduce a number of faults into the system for diagnosis (as described later). The rig also incorporates a number of digital numerical displays to allow an operator to read the temperatures andflowratesas measured by the transducers used. Additional displays show the power input to the electrical water heater in the process tank, the state of the on/off solenoid valves and the cooler. An indicator also shows when the process tank is full to capacity. In addition, a stirrer in the
392
process tank allows the water to be mixed during heating, and a potentiometer allows the operator to manually control the power output of the water heater. The Fieldbus system chosen for this application was a Profibus system, manufactured by Siemens. The system makes use of a bus master, which in this case, is represented by a Siemens PC card of type CP5412 A2. The master controls all the low-level data transfer occuring on the bus. A bus slave is then used to connect to the rig actuators and transducers onto the bus, which in this case, is a Siemens ET200M interface. With these components in place, a Profibus system is capable of high data transfer rates of upto 12Mbaud (mega-bits per second). The Profibus system operates a number of different protocols on the bus, and allows the applications developer to choose one depending on the application involved. The protocol used for this application was Profibus-DP, a cyclic protocol which is commonly used for time-critical data transfers, (Jacob, Ingram & Ball 1996). The Fieldbus system was designed to read all the inputs from the transducers on the rig, and also control all the actuators. Two actuators (the pump and the water heater) required additional control strategies for efficient operation (described later in this paper). Since the Fieldbus system has modules available for analogue and digital input and output, it was convenient to classify the actuators and sensors used on the rig in this way. Tables 1-3 list the sensors and actuators on the rig, and the classifications used.
EXPERIMENTAL INTERFACE CIRCUIT An electrical circuit was designed and built to allow the signals from the sensors positioned on the process rig to be efficiently transferred to the Profibus interface and thence to the controlling PC. In order to retain signal integrity, the electrical circuit was designed to transform all the signals from the rig to a 4-20 mA signal for direct connection to the Profibus interface. Since each type of sensor used on the rig has individual signal characteristics, different methods were used to achieve the signal transformation required. These are also described in tables 1-3. A decision was made to keep all the analogue outputsfromthe Profibus interface uniform as a voltage level in the range 0-10 V. Most of the actuators requiring an analogue voltage input required little or no signal conditioning, however in order to control the output of the pump more effectively, a different approach was required. An electrical circuit to transform the analogue voltage output from the Profibus interface into a pulse-width-modulated (PWM) output was designed, and incorporated into the interface circuit boards. This allowed the pump to be powered from a fixed 15 V supply. In order to operate the solenoid-operated valves on the rig, the cooler and the stirrer, the circuit incorporated a number of electro-mechanical relays, which are controlled by the digital outputs of the Profibus interface. In addition, the electrical heater used in the process tank is powered by a 240 V ac supply, and controlled by a solid-state relay. This relay was in turn controlled by a digital output from the Profibus interface, connected in series with the output from a float switch. This switch is used to indicate when the process tank isfrill.This is provided as a safety feature, to prevent the heater being operated when insufficient water is available in the process tank. Additional circuit boards were designed to provide the connections between the digital outputs from the Profibus interface, the appropriate relay mechanisms and the actuators on the rig. In order to control the power output of the heater, procedures were developed in the controlling software to implement a PWM strategy for switching the solid-state relay. Varying the on/off ratio in intervals of 5% provided effective heater control.
393
A number of features were incorporated into the circuit to afford some protection both to the Profibus interface and also the rig hardware. It was found that under certain conditions, the current transmitters used to transform the signal from the temperature sensors could produce a current output capable of overloading the Profibus interface. To reduce this risk, the circuit included a number of zener diodes positioned on the output of the current transmitters. In order to protect other sensitive parts of the circuit, op-amps have been used as buffers.
DETECTION OF SYSTEM FAULTS The system, comprising of the rig, interface circuit, Profibus interface and PC is shown in diagrammatic form in figure 2. ET200M Profibus interface PROFIBUS CONNECTION
PC equipped with CP5412cardasbus master
Sensors and actuators on rig
Figure 2: The system configuration The system as described above was used to develop fault diagnosis strategies using the sensors and actuators on the rig to simulate an industrial process under test. Faults occurring on the rig can generally be classified into 'hard' faults (often rapid failure of one or more system components) and *soft' faults (gradual degradation of one specific component). Experience has shown that many faults can be detected by monitoring the output of the system controller (in this case, represented by a software-controlled PC) and comparing to the output under 'normal' operation. This method of fault detection was tested with this system, by opening a hand-valve to simulate a leakage in the pipe network. In this case, if the system should provide a constant flowrate of water into the process tank for example, the controller output would be measurably higher when a leak has occurred, and would therefore be an indicator to the occurrence of a leak on the rig. Additional information would be required to pinpoint the position of the leak.
SOFTWARE Software for the collection, display, storage and manipulation of data has been written for the system, making use of the driver fimctions available for the Profibus system. The software was written using the National Instruments Lab Windows development environment, which also offers tools for the design and operation of a Windows user interface. Additional add-on toolboxes provide the system developer with the ability to connect and send data to remote databases, also making use of the ODBC
394
drivers available with the Windows 9x/NT operating systems. The application makes use of the multitasking capabilities of a 32-bit operating system, and uses a separate thread for the collection of data and control of the heater from the main loop, which itself handles all the data manipulation tasks and user interface updates. This system permits the collection of data from the Profibus system to occur at a constant rate, and is unaffected by other tasks being carried out by the main processor. The software was developed with a user interface to mimic the operation of the rig. Animated controls show the operation of the cooler and stirrer. The controls are also capable of indicating when a component operates out of its normal range. A screen showing normal operation of the rig is shown in figure 3.
Figure 3: Software screen showing normal operation Li the context of other work in progress at the IPMM Centre, this system operates as a source of process data. This can be treated as real-time monitoring data, or stored for historical analysis, such as trend or statistical process monitoring. The overall aim of the centre is to provide timely, appropriate information to relevant company personnel regarding the status of the process under observation. To this end, a PC server was built, to handle data from the process, convert this data into usefiil information, and provide personalised web-based display to the end users. In order to reduce the significant workload imposed on the server due to the data processing requirements, a distributed data processing system was devised. This operates by breaking down the data processing tasks into small, manageable operations and distributing these to PCs linked to the server via a network. The client software for this has been written in the form of a screen-saver, and thus makes use of PC processor time when the PC is not in use by the user. This system is illustrated schematically in figure 4.
395
Personalised web based information display, for - Manager - Operator - Maintenance engineers
INFORMATION OUTPUT
DATABASE, DISTRIBUTED PROCESSING AND WEB SERVER - Microsoft SQL Server - Web server software - Custom Distributed Processing software
DATA INPUT
Rig data acquisition system
Figure 4: The information system in use at the Intelligent Process Monitoring and Management Centre To provide the information required by the end users, methods are required for the processing of the data. Work has been carried out using trend analysis and statistical process monitoring in order to detect the 'soft' faults occurring on the rig. Other methods are under development, which will provide a more 'intelligent' analysis of the data. The data acquisition system can itself carry out basic range checking operations prior to sending the data to the server, and could detect the 'hard' faults which result in a failure of one or more components. In order to reduce the volume of stored data, it is possible to use the range checking operations and only store that data which represents abnormal operation. This will also help to reduce the workload imposed on the server. Further work in this area is being planned. Table 1: Analogue input (in terms of the ET200 Profibus interface) Sensor Flowmeter
Sensor output signal Pulsed 5Vfrequencyoutput
Flow temperature
PTIOO resistance output
Manual analogue input Process tank level
Potentiometer resistance output 0-5 V
Process tank temperature
PTIOO resistance output
Sump tank temperature
PTIOO resistance output
396
Signal transformation ADVFC32 (Analog Devices) frequency to voltage converter XTRlOl (Burr Brown) voltage to current converter XTR103 (Burr Brown) current transmitter XTR103 (Burr Brown) current transmitter XTRlOl (Burr Brown) voltage to current transmitter XTR103 (Burr Brown) current transmitter XTR103 (Burr Brown) current transmitter
Table 2: Analogue output (in terms of the ET200 Profibus interface) Analogue output device Flowrate panel meter Flow temperature panel meter Heater power input panel meter Process tank temperature panel meter Sump temperature panel meter Pump
Required voltage range 0-5 V
Profibus signal transformation Resistor network
0-5 V
Resistor network
0-5 V
Resistor network
0-5 V
Resistor network
0-5 V
Resistor network
0-10 V
Analogue signal to Electronic PWM control using control an op-amp circuit
Control strategy
PWM
Table 3: Digital output (in terms of the ET200 Profibus interface) Digital actuator
Actuator input
Cooler indicator
Digital on (5V)
Drain valve indicator
Digital on (5V)
Diverter valve indicator
Digital on (5V)
Tank full indicator
Digital on (5V)
Cooler
High power digital on
Diverter valve
High power digital on
Drain valve
High power digital on
Stirrer
High power digital on
Heater
Mains power
Signal transformation 1 Direct connection to Profibus interface 1 Direct connection to Profibus interface 1 Direct connection to Profibus interface 1 Direct connection to Profibus interface 1 Mechanical relay controlled 1 Mechanical relay controlled 1 Mechanical relay controlled 1 Mechanical relay controlled 1 Solid-state relay controlled
397
Signal control strategy Digital control from Profibus interface As above
As above
As above As above As above As above As above Software PWM controlled via Profibus interface
ACKNOWLEDGEMENTS The authors acknowledge the support of the European Regional Development Fund in the establishment of the Intelligent Process Monitoring and Management Centre at Cardiff University.
REFERENCES Jacob P, higram S and Ball A (1996) Fieldbus: The Basis for an Open Architecture Condition Monitoring Revolution, Maintenance, Vol 11, No 5, pp3-9.
BIBLIOGRAPHY Jennings AD, Kennedy VR, Prickett PW, Turner JR, and Grosvenor RI; A Distributed Data Processing System For Process And Condition Monitoring; to be published in proceedings of COMADEM 2001 Frankowiak MR, Grosvenor RI, Prickett PW, Jennings AD, Turner JR; Design of a PIC based data acquisition system for process and condition monitoring; to be published in proceeding of COMADEM 2001.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
A NON-LINEAR TECHNIQUE FOR DIAGNOSING SPUR GEAR TOOTH FATIGUE CRACKS: VOLTERRA KERNEL APPROACH. F. A. Andrade, 1.1. Esat Department of Mechanical Engineering, Brunei University, Uxbridge, UB8 3PH. UK.
ABSTRACT This paper introduces the usage of a non-linear signal processing technique, namely the Volterra Series, to the field of vibration based condition monitoring. Furthermore, it includes a critical analysis of the performance of this technique on the early fatigue crack identification problem. For this, a spurgear system was used and vibration signatures were collected using both an accelerometer and a stresswave sensor. Finally, the experimental data analysed includes signatures from one gear in good condition and three faulty gears, each with a fatigue crack of different advancement.
KEYWORDS Vibration condition monitoring, non-linear methods, gear maintenance, maintenance, crack identification, digital signal processing.
INTRODUCTION Although it is possible to represent a non-linear system by dividing it into smaller linear, or quasilinear, sections which are then analysed with the already well established theory for linear systems; it has been recognised that non-linear processes can only be analysed as a whole by non-linear methods. Hence the need for the development and testing of non-linear condition monitoring techniques is real. There are a number of non-linear system identification techniques (Billings, 1980) which can be extended to vibration condition monitoring applications. Some examples are: • Artificial neural networks, which has been widely used as a pattern recognition tool to identify and differentiate vibration signatures from rotating devices with different and or multiple faults. This method has been very effective as a post-processing tool for vibration signatures, however it has not been useful for the analysis of the raw vibration signature; • Functional series methods (including Wiener and Volterra series), which allows the user to select the non-linearity order to be included in the system model, and;
399
•
Heuristic methods such as GMDH (Group Method Data Handling) and parameter estimation methods.
Some of these techniques have been suggested and tried in other areas of research, such as communication systems analysis and biological systems modelling. However, from these techniques only artificial neural networks have been widely applied to industrial condition monitoring applications, as a post-processing tool. From this application it was observed that the neural network is performance is very dependent on the pre-processing technique used to extract the relevant information from the time domain time series. The work presented here introduces and performs a critical analysis of one specific non-linear technique, namely: the Volterra Series. It must be emphasised that although this technique has been successfully applied to other fields, it has not yet been used within vibration-based condition monitoring applications. Furthermore, here it is shown that this technique is very effective in the task of identifying fatigue cracks in spur gears. Finally, the thrust behind this work lies in the fact that Volterra series has already been used with some success in the identification of non-linear biological signals. Also, as is shown here, this technique can be used directly on the raw vibration data (and not as a post-processing technique).
VOLTERRA SERIES: CURRENT APPLICATIONS This section reviews some of the current applications of Volterra series. These are grouped into two main classes: non-linear system modelling, and non-linear system analysis and identification (pattern recognition). Previous applications suggest (Marmarelis, 1978) that the main advantages behind the Volterra approach lies in its ability to model mathematical relations between input and output of a given system. Also, the modelling process does not require any previous knowledge about the system structure being studied. Furthermore, by analysing the calculated Volterra kernels it is possible to information related to the underlying non-linear system under study. This information is of utmost importance and enables the condition monitoring engineer to identify faults in machinery. Non-linear system modelling and identification The identification and analysis of non-linear systems plays a vital role in control theory. Most systems encountered in practice are non-linear. By restricting the operating range these systems can be represented by a linear model. Unfortunately, a fact still remains; non-linear systems can only be adequately represented by non-linear models. From the above premise it is essential to analyse non-linear systems by means of non-linear signal processing techniques. Under this scenario, Koh & Powers (1985) showed how Volterra filters (nonlinear filters with the Volterra series structure) can model and predict the dynamic behaviour of moored vessels due to irregular sea waves. His results show the utility of Volterra filters in studying the non-linear drift oscillations of moored vessels when subjected to random sea waves. Koh's work clearly show that the second order Volterra filter is very effective in modelling the lowfrequency drift oscillation of the barge, and also for predicting the barge response. The latter result is of extreme importance in the control and stabilisation of these systems.
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Non-linear system analysiSy and pattern detection and recognition The Volterra series approach has been extensively used in the analysis of non-linear biological and physiological systems (Korenberg & Hunter, 1990). It has been found that its main advantage lies in its ability to model a system with no previous knowledge of its structure. Also, it is shown here how Volterra kernel analysis can lead to inferences of the model structure. Some work in this area has been started by a number of researchers, however the Volterra kernel approach has not yet been applied to vibration condition monitoring. One very strong contribution, suggesting the use of Volterra kernels as a pattern recognition tool was put forward by Bissessur. In (Bissessur & Naguib, 1995) the problem of detecting buried pipes is presented. Initially, an artificial neural network is used to discriminate between ground surface and actual pipe reflection from the return of a radar signal. Later the structure of the trained neural network (i.e. a network that correctly maps the site being surveyed) is compared to that of a Volterra series. It is shown that both the neural network and the Volterra series present the same structure. This suggests that these two systems have strong similarities and can share the same transfer function. Bissessur showed that, by obtaining a mathematical formulation of the weights learnt by the artificial neural network and its nodal functions, it is possible to extract a set of Volterra kernels, formulating the Volterra series representation of a system, in this particular case, the pipe detection problem. These results are supported by those given in (Govind & Ramamoorthy, 1990), which discusses the similarities and differences between neural networks and Volterra series.
VOLTERRA SERIES: THEORETICAL BACKGROUND AND EXAMPLE. In order to describe the theoretical background of the Volterra Kernel approach, this section contains a simple numerical example where the Volterra series is used to model a non-linear time-series. The Volterra series emerged from studies of non-linear functional of the form y(t)=F[x(t');t'0) is dilation factor, b is the translation factor and \\f{t) is the mother wavelet or analysing wavelet. The WT is expanded by a family of kernel functions, which are translated and dilated from the mother wavelet and expressed as:
^„,(0 = - ^ K — )
(2)
where \|/ab is the daughter wavelet and i//{
) represents mother wavelet. An arbitrary time signal x(t) a
at /e[0, T] can be decomposed into summation of wavelets at a finite number of scales as : x(t) = M^,+f^Y^w^,^^W(2't-kT)
where
w, = jx(t) dt
(3)
(4)
w^,^^ = jjc(0 W{2't-kT)
0
dt
(5)
0
Expression in Equation-4 and 5 defining WQ and wj^-k are called the wavelet transform of the signal x(t). In these equations the parameter k determines the position of the wavelet in time and 7, which is called the scale, determines how many wavelets are needed to cover the mother wavelets. TEST RIG AND FAULT SIMULATION The fault was simulated by removal of a percentage of the tooth face width on the pinion gear. Gradual fault advancement was studied by removing face width in 10% increments. Testing ranged from normal condition to 50 % removal of the single tooth. The torque on the output shaft was 260Nm, and other specifications of the gearbox are given in Table-1. The vibration signal generated by the gearbox was picked up by an accelerometer. A reference signal obtained from an optical pick-up was then used to synchronise time domain averaging of the vibration data. The signals were sampled at 6.4 kHz.
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Table-1
Number of teeth Speed of shafts Meshing frequency Contact ratio Overlap ratio
First Stage 34/70 24.3 rev/sec.(input) 827.73 Hz. 1.359 2.89
Second Stage
29/52 6.5 rev/sec. (output) 342.73 Hz. 1.479 1.478
Figure. 1 Test gearbox IMPLEMENTATION OF WAVELET TRANSFORM Many different Morlet wavelet abrupt changes carried out in McDonnel):
possible families of wavelet are available in wavelet applications. In the present study, was chosen as an analysing wavelet since it is well adopted to the problem of locating in the signal (Rioul, Veterli & Morlet). The fast calculation of the wavelet transform is frequency domain. The frequency domain expression of the CWT is (Bentley &
CWT^(a,b) = V^ J x ( / ) H\af) e^^'^^'^df
(6)
Where xff) and Hff) are the continuous-time Fourier transforms of x(t) and h(t), respectively. The calculation is based upon octave band analysis in which each octave is equally subdivided into voices. The calculation of the transform is achieved by implementing discrete version of the Equation.6. The wavelet transform is complex-valued and is presented as a contour plot of its magnitude (modulus) and phase. Now the ability of the CWT will be examined by using the magnitude and phase information of the CWT for gearbox diagnostics. Figure.2 displays the phase and magnitude map of the CWT for various conditions. It demonstrates the ability of the WT to detect and localise the significant change in the vibration signal. Both the phase and amplitude map of the WT reveals the growing local fault in the gearbox. In the phase plot of the WT for healthy condition, the fundamental and second toothmeshing frequency are visible, though the energy of the vibration signal mainly concentrated around the fundamental meshing frequency. Although some change occur in the phase plot of 10 and 20 % fault conditions, most of the signal energy is still concentrated over the fundamental meshing frequency. Therefore, the fault conditions are not clearly identifiable at this stage. From 30 % fault conditions onwards, most of the signal energy appears to be (500 Hz) outside the fundamental meshing frequency and this indicates the presence of a fault in the gearbox. Similarly, the amplitude plot of the WT illustrates that when there is no fault (or a very small fault) the energy of the signal concentrates over the fundamental meshing frequency. With the growing fault the energy of the signal shifts significantly outside the meshing frequency region. The amplitude map seems to be easier to interpret though both, the phase and amplitude of the WT reveals the fault conditions. USING PRINCIPAL COMPONENTS AS A PREPROCESSOR IN WAVELET TRANSFORM A pre-processing of the signal based on principal components analysis (PCA) is introduced before the wavelet transform. The PCA is a statistical technique that transforms correlated original variables into a new set of uncorrected variables using the covariance matrix (or correlation matrix). If observation
413
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0
The co-ordinate values on >'o^2^o ^ ^ ^QA
A),\ -
cos 6^5
yo,i = sin ^5
0
0
- sin ^5 cos ^5
0
0
0 (17)
0 •4,1 + A 1 0
* Fig. 3 appears at the end of the paper
421
•^0,2
A),2 ~ yoj _0
cos ^6 = sin ^6
0
- sin 0(^ 0 cos ^6
0
0
1
(18)
Here, A is the displace O^Oj between rotating axle centres. If the co-ordinate values (x, ,,;^,,) on the driving gear (normal gear) and rotating angle 0^ are given, the meshing point (Xj^, yia) on driven gear (abnormal gear), which contacts with the point (x,,, j^,,), can be calculated by ^0,1 =^0,2' yo,i =yo,2 X,, cos ^5 - y,, sin ^5 = X22 cos ^^ - y2 2 sin ^^
(^^^ (^^)
;c,, sin^5 + >^,, cos^j +A = x, 2 sin^^ - >'2 2 cos^^
/2 j \
The unknown parameters in the above equations are X2 2, J^2.2 ^^^ ^6 • ^ ^ can substitute i9(, to the equations sequentially, and search the point (^32, 3^2,2) ^^m formulae (17) and (18) that satisfy formulae (20) and (21). Then the co-ordinate value (Xcn^oj) ^^ meshing contact point can be obtained.
RESULTS OF SIMULATION AND EXPERIMENT In order to verify the efficiency of the method proposed in this paper, we used the rotating machine shown in Fig. 4 to measure the vibration signals of the gear equipment in normal state, eccentricity state, spot flaw state and wear state. The specification of the gear for the experiment is shown in Table 1. Table 1 accelerometer
Specification of gears for simulation and test Module
2
Width of the tooth
20(mm)
Pressure angle (a)
20°
Number of teeth (normal)
55
Number of teeth (wear)
75
1 Backlash of the normal
the gear for the testgear box
k-load
belt
0.5(mm)
gear Load torque
motor
1.5(N.m)
Fig.4 Rotating machine for tests Normal state Fig. 5 (a) and (b) show the waveforms obtained by simulation and experiment. It can be observed that the results of simulation and experiment are approximately alike in positions of the peak and the form. Eccentricity state Fig. 6 shows the eccentric gear. Fig. 7 shows the waveforms obtained by simulation and experiment. In the same way as the normal state, the results of simulation and experiment are approximately alike in positions of the peak and the form. 422
Wear state In this paper, we show two types of wear state as shown in Fig. (8) and (9). The driving gear is in normal state, and the driven gear is in wear state. Fig. 8 shows the worn gear surface that is approximately expressed by a plane. Fig. 9 shows another type of worn gear surface that is decided by the slip ratio between two teeth during meshing. Fig. 10 shows the waveforms of worn gear obtained by simulation and experiment with the rotating speed lOOrpm and 200 rpm. In the same way as the normal state and eccentricity state, the results of simulation and experiment are approximately alike in positions of the peak and the form.
0.02
TuTie(sec) (a) Tu-ne wave of normal state by simalation (lOOrpm)
200rpm), the position of the feature peak in the spectra is at the rotating frequency. But in the case of low rotating speed ( > > > >
the appropriate form of information delivered at a consistent and regular periodic time to the right person at the correct place in a secure and reliable form
The presentation of information shown to users has to be understandable to the majority of employees irrespective of their knowledge and place in the company hierarchy. Therefore, simple graphical charts and pictures will be presented. The recipient of the information receives a personalised delivery. This provides users with a personal attachment and apathy. Also, the nature of the information delivery system (i.e. to provide regular directed and structured information) provides a tool to aid interim and immediate reinforcement of company goals. Makin et al., (1996) suggest that interim reinforcers help maintain long term goals while inmiediate reinforcers can be stronger in their influence on individuals than delayed reinforces. An example of this is a firm trying to increase its production output by 20%. The achievement of the project may be forecasted to take the business 12 months. The performance indices shown in Eqn. 2 can be used to provide regular updates and help manage and control of equipment. Providing the information to enable immediate and interim reinforcement. CONCLUSIONS The purpose of the information delivery system is to take isolated maintenance derived data from the maintenance department, combine it with various sources of business data and then provide and translate it into useful information to all employees regardless to whether they have any existing maintenance knowledge. Unlocking this potential source of information using the adoption process that has been stated enables proactive maintenance decisions to be made through a business. The Key Performance Indices provide the type and direct relevance of information individuals and therefore the delivery system require. This tailored information allows individuals to make empowered decisions because instead of being told what they have to do, they are provided with the information to make their own informed choice. The Key Performance Indices also provide the requirement for the exact sources of defined data. This data has to be controlled and monitored to prevent poor or incorrect data being placed into the system. Conununication within the enterprise is another beneficiary of the use of the system. The information provided can break through functional and departmental boundaries. Allowing a new understanding of maintenance and wider management issues to be received by all employees.
479
The integration of computer systems on a business is highly expensive. Using the techniques described the minimum disruption is caused providing no negative reaction to the introduction of the technology prior to the system starting operation. The final conclusion is that a high level of trust and confidence has to be attained for successful adoption. This can be enhanced by features supplied within the system itself and by the support offered from top management. The uptake can be measured by reviewing overtime of whether or not individuals actually read the information. This performance information can provide the means to improve and adapt the system to the changing requirements of individuals.
REFERENCES Beyond ERP, Manufacturing Engineer, 79:5, October 2000, 210-213. Kelly A. (1997). Maintenance Strategy - Business-centred Maintenance, ButterworthHeinemann, Oxford, UK. Makin P., Cooper C. and Cox C. (1996). Organizations and the Psychological Contract, The British Psychological Society, Leicester, UK. Wireman T. (1998). Developing Performance Indicators for Managing Maintenance, Industrial Press, Inc., New York,
ACKNOWLEDGEMENTS The author would like to thank the support and advice of Wolfson Maintenance in the production of this paper. Specifically Dr I. Kennedy, Dr A. J. Doyle and Dr M. Niblett for their input and insight into some of the ideas behind this paper.
480
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DESIGN OF A PIC BASED DATA ACQUISITION SYSTEM FOR PROCESS AND CONDITION MONITORING M.R. Frankowiak, R.I. Grosvenor, P.W. Prickett, A.D. Jennings, J. R. Turner Cardiff University, School of Engineering, Newport Road, Cardiff, CF24-3TE, Wales
ABSTRACT This paper describes the design of a data acquisition system, based upon a Microchip PIC microcontroller, that is being developed for condition and process monitoring. It presents the considerations made in the development of hardware and software to allow the deployment of a monitoring system based upon the Petri-net concept. The methods by which the data collected by the system can reach a centralised database are described. In doing so consideration is given to the available connectivity options for the micro-controller based monitoring system used. The benefits and limitations of such a micro-controller based system are discussed using some initial results obtained by the experiment. KEYWORDS Monitoring, Micro-controller, Petri-net, CAN bus, Internet. INTRODUCTION This paper describes the use of a micro-controller to perform monitoring tasks based upon the concept of Petri-nets. This work is part of the research currently being conducted by the Intelligent Process Management and Monitoring (IPMM) Centre of Cardiff University. The aim of the IPMM Centre is to promote the development of process monitoring technology. Initial consideration of the deployment of a micro-controller based monitoring system provided sufficient success to support this development (Christopoulos 2000). It is based upon the use of Petri-nets for monitoring purposes, which is an established part of the IPMM research activities (Prickett et al. 2000; Jennings et al. 2000). New and powerfiil devices have been released by the micro-controller industries. These are being used, in work such as that described here, to support applications that previously demanded a much greater level of resources. In the case of process monitoring the cost of these existing systems often prevented their widespread adoption.
481
The PIC (J^ Microchip Tech. Inc.) family of micro-controllers is part of this new generation. A considerable number of applications based upon them have been developed, taking advantage of their reduced size and power consumption, as well as affordable prices. This paper outlines the development of monitoring hardware based upon the PIC family's provision of digital and analogue inputs, as well as communication facilities; PIC micro-controllers are provided with a subset of devices that enable their integration using a Control Area Network (CAN). CANs are widely used by industry and are one of the most accepted Fieldbus standards (Scott and Buchanan 2000; Rufmo et al. 1998). The work also considers the management of the data generated by the monitoring system. This is normally uploaded to a remote database through the Internet. Internet connectivity has to be considered (Prickett et al 2000; Kennedy et al. 2000). It represents the natural way to get access to data collected by distributed monitoring systems. It also promotes the use of powerful computational structures, which can be far awayfromthe monitored process, but can be used to handle the data in an appropriate manner. The Internet also offers the facility to make use of an existing and well developed structure to access the process information from anywhere. The amount of program and application memory required to support these frmctions and the ability of PIC based systems to meet these needs is considered below. A PC based Petri-net monitoring tool previously developed within the IPMM Centre enables the modelling of a process as a sequence of events (Prickett et al. 2000; Jermings et al. 2000). The same technique was considered to be a good basis from which to develop a monitoring system based on a micro-controller. A Petri-net model represents a logical and clear way to describe a process. It allows hardware and software independence, once the process modelling the Petri-net can be easily integrated into the existing hardware and software. If the process changes the model can be edited, but the monitoring system does not change. Limitations associated with the number of input lines for monitoring purposes, as well as the size of process to be monitored are considered in detail below. HARDWARE CONSIDERATIONS The PIC family of micro-controllers offers a range of devices to suit a number of applications. This project is currently using the PIC18C452 device. The most attractive features of it, from the point of view of this application, are the digital I/O ports, the analogue inputs, the programmable timers and serial communication ports. These features are software configurable and their number and availability may vary according the application. Equally important are the size of the available program and application memory, last one designated as file registers. Full details are available at Microchip homepage (2001). The concept of Petri-nets for monitoring utilises signals driven by the process to control the firing sequence of the model's net transitions. By using multiplexing techniques, a total of 24 digital inputs are allowed through one of the 8 bit ports of the PIC chip. Four analogue and two pulse inputs were also planned to enable some other more complex monitoring fiinctions. Currently a Universal Synchronous Asynchronous Receiver Transmitter (USART), which allows a wide range of configurable baud rates, is connected to a standard RS-232C communication port on a PC-based computer. Monitoring data collected by the system is displayed on the PC and can be transferred to a centralised database server. As a next step, a CAN bus will be used to enable distributed system integration, as well as to establish a centralised node which will be responsible for data transfer to an existing database server cormected to the Internet. Figure 1 shows a block diagram of the hardware structure. Despite the fact that this specific chip requires a clock up to 40 MHz, the system was designed to operate based on a 20 MHz clock. This lower frequency was chosen to be within the limits imposed by 482
the CAN controller (MCP2510), avoiding more then one oscillator on the circuit. One of the embedded internally clocked timers was configured to generate a time base that updates a real time feature. Interrupts are generated at periodic intervals to read and store the digital input levels, without requiring special software polling to perform it. All peripherals were configured to request service through interrupt lines. Software construction becomes easier, once specific blocks are linked to the respective peripheral requesting actions.
RS-232C Interface
I
M
8 X digital inputs
I
M
8 X digital inputs
jj
8 X digital inputs
P I C
CAN Node
4 X analogue inputs
^
2 X pulses inputs
Figure 1: Basic hardware structure, with a proposed CAN node
SOFTWARE CONSIDERATIONS The PIC18C452 micro-controller uses 16 bit wide instructions, offering a large range of instructions and consequently making programming easier. A number of instructions for data manipulation are also available, which is ideal for applications like the one under consideration. The basic structure of the software developed is shown in Figure 2. The initialisation block sets up the micro-controller operating mode and selects and configures the peripheral devices used by the application. The main body of the application software runs continuously, checking the existence of a new event that may require the system intervention. To make fiill use of the available resources the application uses interrupt services for the peripheral devices, such as timers and communication interfaces. These devices request the specific and appropriate intervention only when necessary, avoiding any unnecessary processing. Table 1 presents the description of the tasks performed by each of the main components shown in Figure 2. Initialisation Block
: : : :
Monitoring Net Checking
Timer Interrupt
Command Analysis
Receive Data Interrupt
Message Construction
Transmit Data Interrupt
1 1 : ;
: Main Body
Interrupt Services
Figure 2: PIC based monitoring software structure 483
TABLE 1 APPLICATION SOFTWARE COMPONENTS DESCRIPTION Description
Software Component Net checking
The transitions are checked to detect whether or not the input states matches with a firing condition. When selected, a message to notify the event is requested. Affected places are updated.
Command analysis
A set of commands is provided to perform some basic tasks. It includes set date to set up the calendar date, set time to initialise the real time clock and the reset command, which forces the system to return to the start point of the Petri-net.
Message construction
When a transition is enabled, a message indicating that a token has been fired is written and loaded in a buffer, accessed by the data transmit interrupt service. This message contains the transition number and date and time associated with the event.
Timer interrupt
Updates the system real timer and calendar. The state of the sensors monitored is also updated by the interrupt.
Receive data interrupt
Stores received data in a system buffer.
Transmit data interrupt
I Updates the system real timer and calendar. The state of the sensors monitored is also updated by the interrupt.
PIC BASED MONITORING A Petri-net monitoring system based on an embedded micro-controller like the PIC18C452 can represent a cheap and easy to install solution. It is important that the monitoring hardware and software implementation should not be dependent upon the process if we are to avoid the necessity of providing a new hardware/software set-up for each different process. To prevent this from happening, the Petrinet describing the particular process is placed into a separate data table. The use of data structures with the PIC18XXX family is facilitated by the existence of a set of instructions to retrieve data from a table. Thus a new Petri-net can be created to monitor any new or changed process, input into the data table and integrated into the PIC based monitoring system. At the moment this data table has to be compiled together with the application software. However the development of a PIC micro-controller, based on flash memory technology will result in a more flexible, user-friendly alternative system. The Petri-net monitoring tool developed and deployed within the IPMM is based upon an adaptation of the original theory (Peterson 1981). This allows enabled transitions to be associated with process events using signals provided by existing sensors (Pricket et al. 2000; Jennings et al. 2000). Figure 3 represents a generic example of this kind of situation. The sensors have their state regularly polled to detect a firing condition. All input conditions must match to enable the transition to be fired. Monitoring is thus based upon the firing of transitions. The model produced to enable a Petri-net to be designed for use with a system using a PIC microcontroller has a significant difference. It considers transitions as static and places as dynamic entities and thus monitoring is based upon the movement of tokens through places. This consideration means
484
that places can to be separated from the structure that defines the model and is controlled by the transitions.
®
Input Place - with a token External Sensor
Transition
O
Output Place
Figure 3: Adapted Petri-net transition for monitoring purposes Figure 4 shows a block diagram representing the considered data structures. The transition data structure basically points to a sensor mask (i.e. identifies the source of input signals), and to input and output places for each transition. Places are thus seen as individual entities that can have the number of tokens they contain incremented or decreased, depending upon their condition regarding the fired transition, i.e. inputs from transitions into places increase the number of tokens present in any place, whilst outputs from places to transitions decrease the number. Transition Identification Sensors Mask Input Place Pointer
End of Input Places Output Place Pointer Token Identification End of Output Places
Number of Tokens
(a)
(b)
Figure 4: Transition (a) and place (b) data structures An important observation is that transitions are considered static because they do not change during the monitoring process. Even though transition structures can vary with the different numbers of places and inputs from one application to another. Places within the Petri-net will however exhibit changes to the number of tokens they contain, which will vary with time and with the particular route taken by the tokens through the Petri-net. 485
When a transition is enabled and a token is fired, the associated data string is sent to a PC computer, where a dedicated application, connected to a remote server, records the event in a database. Figure 5 shows the PC application window, associated with this task. Commands to set the date and time, and restart the Petri-net running on the PIC are available on the application window. Functions to make the database selection are also available.
C^^B^^^^^^^^^^HI l^^nSfTI \Be
Edil Configure
Fired Transition TOlO 15:29:17
[ S.ET TIME 1 1 £ET DATE I
[ BESET )
@)
Figure 5: Transition firing PC application window In the database the transition number, date and time of the event are recorded in separate columns, as shown in Figure 6. Data analysis is possible through the use of traditional tools.
TIME liiLmtMl 15:27:15 TD01 TD03 7039 TD10 TD15 TD01 T004 T009 TD10 •rai5
LLI
15:27:41 15:28:11 15:29:17 15:30:42 15:30:42 15:32:25 15:33:05 15:33:28 15:34:18
DATE 21/D4/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21A34/2001
' ' '"^'>^'
r• '
d
Figure 6: Database table The experiment that originated the data shown in Figure 6 was based on a scale module of an ASEA hydro forming press, which is controlled by a Siemens S5-95U PLC. A Petri-net with 14 places and 15 transitions, monitoring 7 external digital signals, was used to describe the sequence of events present on the process. The number of places and transitions represents 5% of the total capacity of the proposed system. Despite the simplicity of the process that was monitored, these numbers show the capabilities of this kind of monitoring solution. The use of more than one monitoring module on the same process or machine is possible and is under development at this moment. Figure 7 shows the equivalent Petrinet designed usmg a PC computer based application developed by the IPMM Centre (Jennings et al. 2000). Since the data is available in a database server connected to the Internet, application based on the e-monitoring (Kennedy et al. 2000) concepts can extract the information necessary for management purposes.
486
Figure 7: PC based Petri-net monitoring tool The project is being further developed to utilise the newly developed PICs to produce monitoring devices that can access the database without the intervention of the PC computer playing a role as a gateway. It demands that the monitoring system based on the PIC micro-controller must be able to get access to the Internet. Several cases have been reported showing the use of PIC micro-controllers with Internet applications (Microchip web site 2001). Nevertheless it is still a challenge to provide Internet access protocols and monitoring tasks running on the same system. The different nature of the applications and the amount of resources demanded, specially file registers, suggests the most promising way forward is to deploy separate systems, one dedicated to the monitoring task and the other for Internet access. CAN is seen as offering potentially the best choice to inter-connect these communication and monitoring systems. In this way it may also be possible to link multiple systems across more complicated processes. This technology is supported by the PIC family of microcontrollers and the standard and the support necessary for its use are already available. This network tends to be relatively small, restricted to the process and with small size of data strings to be transmitted. All these fit quite well with CAN.
FINAL CONSIDERATIONS Petri-nets for monitoring purposes, based on micro-controller, are a useful tool. The challenge now is to develop this technique and build a monitoring system that allows the deployment of distributed monitoring along the process or at different parts of a machine. This system must then collect the necessary data and provide tools allowing data analysis and communication. The benefits of monitoring techniques are already well known. The use of the new developments, like those based on PIC family of micro-controllers, can bring some good options for a wider employment of monitoring, merging the benefits of distributed applications with those of reasonable cost. A considerable amount of research is being conducted to enable embedded systems to get connectivity facilities, especially those related with the Internet. It denotes that very compact systems will have an 487
important place in the development of a wide range of applications. Nevertheless, research has to be made to allow all the different and necessary parts involved to be joined together, ensuring a reliable structure that can be expanded and configured to suit a range of applications. ACKNOWLEDGEMENTS The authors acknowledge the support of the European Union via the ERDF grant to establish the Intelligent Process Monitoring and Management Centre. M. R. Frankowiak is a PhD student supported by CAPES, a Brazilian Federal Agency for Post-graduation Education. REFERENCES Christopoulos A. (2000). PIC Microcontroller Application. Project Report. Cardiff School of Engineering. Jennings A.D., Nowatschek D., Prickett P.W., Kennedy V.R., Turner J.R. and Grosvenor R.I. (2000). Petri net Based Process Monitoring. In Proceedings ofComadem 2000. Houston, USA: MFPT Society. 643-650. Kennedy V.R., Jennings A.D., Grosvenor R.I., Turner J.R. and Prickett P.W. (2000). Process Monitoring Using Web Pages (e-Monitoring). In Proceedings ofComadem 2000. Housto, USA: MFPT Society. 877-883. Microchip Technology Inc (2001). Microchip Technology products. Microchip Website. Arizona, USA. Availablefrom:http://www.microchip.com/14010/helper.htm. [Accessed 02 April 2001]. Peterson J.L. (1981). Petri Net Theory and the Modelling of Systems, Englewood Cliff, Prentice-Hall. Prickett P., Grosvenor R., Jennings A., Kennedy V., Nowatschek D. and Turner J. (2000). Developing an Internet Based Intelligent Process Monitoring and Management Centre. 3'^ International Conference on Quality Reliability and Maintenance. Oxford: IMechE ISBN 1860582567, 79-82. Rufino J., Verissimo P. and Arroz G. (1998). Embedded Platforms for Distributed Real-Time Computing: Challenges and Results. In Proceedings of the Second IEEE International Symposium on Object-Oriented Real-Time Distributed Computing. Saint Malo, France: IEEE Comp. Soc, 147-152. Scott A.V. and Buchanan W.J. (2000). Truly Distributed Control Systems using Fieldbus Technology. In Proceedings off^ IEEE International Conference and Workshop on the Engineering of Computer Based Systems. Edinburgh, Scotland: IEEE Computer Society. 165-173. BIBLIOGRAPHY Turner JR, Jennings AD, Prickett PW and Grosvenor RJ; The design and implementation of a data acquisition and control system using fieldbus technologies; to be published in proceeding of COMADEM2001. Jennings AD, Kennedy VR, Prickett PW, Turner JR, and Grosvenor RI; A Distributed Data Processing System For Process And Condition Monitoring, to be published in proceedings ofCOMADEM200I.
488
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
APPLICATIONS OF DIAGNOSING OF NAVAL GAS TURBINES Adam CHARCHALIS Mechanic-Electric Faculty, The Naval Academy Gdynia, Poland,
ABSTRACT A high combat readiness is the fundamental feature of contemporary vessels from cutters to aircraft carriers. It results from introducing the new fighting means and from the application of small-overall dimensions, small mass and high power gas turbine engines for a vessel propulsion system. The gas turbine engines are used on various types of vessels from cutters to aircraft carries. They represent a part of the homogeneous propulsion as well as combined propulsion. Nowadays they are applied for propelling high speed vessels in all significant world fleets. The application of turbine engines as the main propulsion engines of a vessel impels, according to requirements, operation procedures. It alters considerably the usage of gas turbine engines in the navy from the ones in aviation. The relatively low load is characteristic to the operation of the marine engine. The time of operation on average and maximum load constitutes only up to several per cent of the whole time of its operational usage. That is the reason why the efficiency of operating marine engines is considerably lower than the planned (designed) efficiency. The paper presents general possibilities of identification of the operating states of naval gas turbines by the Base Diagnostic System application. The system configuration, testing means and methods are demonstrated within the paper. The system was implemented into the Polish Navy Vessels with turbine engines. Measuring positions, the data base constantly being updated and diagnostic software allow carrying out the operation of turbine engines according to their actual conditions.
KEYWORDS Naval gas turbine, technical diagnostics, diagnostic system, testing means and methods, diagnostic software.
BASE DIAGNOSTIC SYSTEM OF NAVAL GAS TURBINE ENGINES The usage of naval gas turbines requires a professional technical supervision. Such a requirement caimot be fulfilled by crews of small vessels which are mainly provided in the Polish Navy, Therefore, it was decided to support the crews of such vessels by the „Base Diagnostic System of Naval Gas
489
Turbine Engines" (BDS) [3,5]. The system is introduced for periodical inspections of engine condition and particularly in case of: • annual maintenance, • necessity of the prolongation of mean time between major repairs, • identification of an abnormal running found during routine maintenance. The Base Diagnostic System consists of a serious of diagnostic positions and provides the possibility of complex examination of engine conditions by EDP (Electronic Data Processing) application. The BDS (consequently) is capable of working out the prognosis for the engine future operation. An operating decision is worked out on the basis of appropriately prepared measurements of the engine parameters. They are subsequently converted into diagnostic parameters according to the elaborated flow diagrams for computer programs. It was established during the BDS creation that diagnostic information would be gathered: • systematically - from the operating documentation of the vessel (an engine log-book), • periodically - by an automatic measuring-registering device, • periodically - by examination of special parameters describing the conditions of an engine e.g. measurement of vibrations and impurity of oil, endoscopic examination, registration of start-up and lay-off processes and so on, • periodically - on the basis of interviews with experts and expert's opinion. The BDS enables accomplishing the following tasks: a) detection of the engine conditions that may lead to defects and even to the break-down, b) making a diagnosis of the ship's supervising-measuring system, c) carrying out a current evaluation of the engine characteristics - fouling intensity of engine passages and, if it is necessary - restoring to the engine original state by washing the passages, d) keeping data base of each engine and vessel and making a prognosis of changes of the engine operation condition. From the technical point of view the BDS is equipped with a special supervising-measuring device capable of carrying out numerous (foregoing) tasks on the grounds of measured values of various parameters of the engine. The system configuration is based on operating experiences from aviation but the computer measuring system, diagnostic software, research methods and data base are original. In order to work out all tasks securing the proper gas turbine operation the BDS is equipped with following special apparatus: 0 for measurements of vibration parameters and their analysis, 0 position for oil examinations on metal particle contents and other impurities, 0 computer measuring system of start-up and lay-off parameters, 0 computer measuring system of operational parameters, 0 programmable analyser of high-changeable signals, 0 endoscopes, 0 automatic test equipment for safety devices and supervising-measuring apparatus of the engines, 0 computer data base. The methods of gathering of diagnostic parameters for the condition evaluation of different engine's sub-assemblies are presented in Figure 1.
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GAS PASSAGES TESTING OF SAFETY DEVICES AND SUPERVISING-MEASURING APPARATUS
ENDOSCOPIC EXAMINATIONS
AUTOMATIC CONTROl ' SYSTEM OF THE ENGINE
'-^''.c: - 4^F!55S^.^ MEASUREMENT OF THERMODYNAMIC PARAMETERS OF THE GAS COMBUSTOR WITH ELEMENTS OF THE ENGINE AND POWER PLANT FUEL SYSTEM
ACCElfiRATION ©ICELIRAHON CONTROL SHAFTING AUGMENT
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^T KINEMATIC SYSTEM WITH ELEMENTS OF THE ENGINE AND POWER PLANT OIL SYSTEM
T
MEASUREMENT OF TEMPERATURE INEQUALITY OF THE EXHAUST GAS
/cS'^Kf;^;i^,twl
MEASUREMENT OF THERMODYNAMIC p i PARAMETERS OF THE GAS
fAAAA EXHAUST GAS ANALYSIS MEASUREMENT OF PHYSICAL AND CHEMICAL LUBE OIL PARAMETERS , MEASUREMENT OF FUEL CONSUMPTION VIBRATION MEASUREMENT AND THEIR SPECTRAL-CORRELATION ANALYSIS VIBRATION MEASUREMENT OF THE FUEL SYSTEM ELEMENTS
..^^^^
^.^^iiw^^jr
ENDOSCOPIC EXAMINATION
Fig. 1. Methods and means of gathering diagnostic parameters.
COMPUTER MEASURING SYSTEM FOR EVALUATION OF PROPULSION CHARACTERISTICS AND OPERATING CONDITIONS OF A COMBINED PROPULSION SYSTEM The measuring system gathers diagnostic information about the elements of a propulsion system and allows an evaluation of propulsion characteristics in different floatation conditions. The system has been designed with perennial experiences of the naval gas turbines diagnosing [7]. The system is capable of simultaneous measurement of operating parameters (about 160 parameters) of four turbine engines operating within COGAG propulsion system and ship motion parameters. In comparison with the measuring set of the engine start-up it is extended with measurements of a ship velocity, fuel consumption, mass flow rate, exhaust analysis and thermal field. Figure 2 shows a diagram of the gathering of diagnostic parameters for marine power plants with gas turbines.
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The magnitudes measured during experimental sea trials are the base for calculations of those diagnostic parameters which are able to estimate the condition of the engine passages and make it possible to elaborate an adequate operating decision concerning maintenance activities, connected with the restoration of the propulsion characteristics [1,9]. An example of the exhaust temperature distribution in gas passages behind the gas generator for different engine loads is presented in Figure 3. These distribution diagrams are used to make diagnosis of the engine fuel feed system.
EXHAUST
Fig. 2. Diagram of measurements of naval gas turbine.
-0.8N -1.0N -1.2N ~ 605 oC 695 oC 710 oC 0.4 N 0.6 N 555 oC 580 oC
Fig. 3. Exhaust temperature distribution in the gas generator outlet. The measurements enabled the calculation of changes of those parameters which describe the energy states along the passages. These changes were then used to estimate the quantitative and qualitative influence of engine condition on the compression and expansion processes. The examination of the conditions of engine passages showed that in order to make a diagnosis it is necessary to consider changes in slip (considered as a difference between HP and LP shaft speed) between measured and design values: 6(AnL.p) [3]. Since, at a given setting, the fuel control system ensures that nHp remains constant at its design value, this evaluates as: ^^U'i
where: Art/j, = n,P
{measured)
'lA\clesisn)
0 and 1 are the analysed states of the engine
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COMPUTER DATA BASE The computer data base, built on the grounds of IBM-PC computer, consists of: • files, containing memorised information which is systematically and periodically led into the data base, • programmes which are able to memorise the information and transform it into parameters diagrams and decision tables. By this means the diagnosis of the actual operating state of the engines is made easier. The data base consists of five fundamental programmes for data leading from: • measurements with the computer measurement system - RAMB, • the ship's operating documentation - BDOST-U, • oil examinations - BAZ-DOL, • the vibration measuring system - VIBRATION, • thermoscopes - TEMPERATURE. All of these programmes allow the preliminary analysis of data, an elaboration of results tables and diagrams. DIAG-C programme provides the shattering of measuring data files, related to load: continuous running and start-up. BAZ-DM programme provides connection between data taken from measurements with the computer measuring system and the results led from the operating documentation. A comparison of various diagnostic parameters is carried out after that. ROZRUCH programme conducts the parameters computation of start-up and lay-off parameters in options as follow: „cold" and „hot" start-up and lay-off The programme evaluates a state trend for the engine subassemblies. The transformed information from the programmes: „ROZRUCH", BAZ-DM", „BAZDOL", „DRGANIA" and „TEMPERATURE" is transferred to EXPERTISE" programme which provides comparison to the model data and by means of that - takes an operating decision. The block diagram of the data base organisation is presented in Figure 4.
REFERENCES 1. Charchalis, A., Korczewski, Z., Diagnostics of gas turbine passages on the grounds of measured thermogasodynamical parameters, Modelling, Measurement and Control 1994, C, Vol. 40, Nr 2, France (in English). 2. Propulsion systems of fast vessels with turbine engines, Marine Technology Transactions Polish Academy of Sciences Gdansk vol. 4, 1993, pp.38-48 3. Charchalis, A., Diagnostic system of naval gas turbines, Przeglqd Mechaniczny, 1993, Nr 1, pp. 4-5, Poland, (in Polish). 4. Experimental diagnostics of naval gas turbines, ISROMAC, Honolulu 1998, s. 5. Charchalis, A., Measuring systems applied in marine gas turbine diagnostics, Internal publication of the Polish Naval Academy, 1993, Nr 903, pp. 75-84, (in Polish). 6. Charchalis, A., Mironiuk, W., Szubert J., On the basis of vibration measurements. Internal publication of the Polish Naval Academy, 1993, Nr 903, pp. 109-122, (in Polish). 7. Charchalis, A., Computer measuring-diagnosing system of naval gas turbines, WOS£ publication, 1993, Nr 1 /U, pp. 101 -106, (in PoUsh).
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VIBRATION MEASUREMENT SYSTEM
OIL EXAMINATION ^^l^,*,?r^- r
BAZ-DOL
)
IMPURITY PARAMETERS
TEMPERATURE DIAGRAMS
MODEL DECISION TABLES
f VIBRATION 1
VIBRATION PARAMETERS AND ANALYSIS
{TEMPERATURE)
THERMOSCOPE READ-OUT
^DECISIONN^
Fig. 4. Block diagram of the data base organisation.
Charchalis, A., Korczewski, Z., Dyson, P., Evaluation of operating conditions of the passages of naval gas turbines by gas path analysis. The Institute of Marine Engineers London, England, 1996, (in English). Charchalis, A., Grz^dziela, A., Diagnosing the ship shafting alignment by means of vibration measurement IMAM2000 - Naples
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
DIAGNOSING OF NAVAL GAS TURBINE ROTORS WITH THE USE OF VIBROACOUSTIC PARAMETERS Adam Charchalis, Prof, and Andrzej Grz^dziela, Ph. D. Polish Naval Academy, Gdynia Institute of Construction and Propulsion of Vessels
ABSTRACT In the paper results are presented of vibroacoustic research on balance control of gas turbine rotors and assessment of their permissible operation times. In the paper results of vibroacoustic research on balance control of gas turbine rotors and assessment of their permissible operation times are presented. The work is a part of implementation of an the integrated diagnostic control system of gas turbines installed on Polish Navy ships fitted with COGAG propulsion systems.
KEYWORDS diagnostic, vibration, gas turbine, rotors, balancing
INTRODUCTION Exploitation of naval ship propulsion systems is a complex task due to specific features of marine environment as well as demand of maintaining high level of serviceability and reliability of the ships [1]. From the side of operators, doubts are often expressed concerning maintenance times or making decision on further exploitation of the object. The vibroacoustic monitoring systems of ship propulsion systems were first time applied in the middle of 1980s [5,7,8]. Application of periodical diagnostic procedures or on-line monitoring systems makes it possible to operate ship propulsion systems in accordance with their current technical state. In the case of ship gas turbines the hourly period of scheduled maintenance or repair surveys is presently the criteria for maintenance time determination. Though such exploitation strategy makes early scheduling of maintenance operations and their logistic assurance possible, but it simultaneously contributes to increase of costs because of its replacement system of elements (technically often still serviceable ones) as well as it makes impossible to early detect primary symptoms of failures occurring before the end of maintenance time [2,3,4]. Application of vibroacoustic techniques to the gas turbine propulsion systems, as an element of multisymptom diagnostics, is justified not only regarding the same turbines but also for investigations of
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mutual geometrical position of elements of torque transmission system, as well as of control of electric energy generating sets.
OBJECT OF INVESTIGATIONS The 1241 RE missile corvettes, among other Polish Navy ships, are also subject to a permanent basic diagnostic system. They are fitted with COGAG gas turbine propulsion systems. Their configuration scheme is presented in Fig. 1.
Fig. 1. A scheme of the propulsion system of 1241 RE missile corvette 123456789-
Starboard service turbine unit ( TZSM PB) of abt. 3000 kW output Starboard service reduction gear ( PM PB) Starboard peak-power reduction gear (PMS PB) Starboard peak-power turbine unit (TZSS PB) of abt. 9000 kW output Port side peak-power turbine unit (TZSS LB) of abt. 9000 kW output Port side peak-power reduction gear ( PMS LB) Port side service reduction gear ( PM LB) Intermediate shaft Port side service turbine unit ( TZSM LB ) of abt. 3000 kW output
To obtain reliable data on diagnostic parameters, investigations of the gas turbines installed in the presented propulsion system were carried out by means of the multi-symptom diagnostic model whose one of the main features is recording and analysing vibroacoustic signals. The investigations were aimed at determination of permissible in-service unbalance and appropriate assemblage of turbine rotors on the basis of selected vibroacoustic parameters, and - finally - determination of their permissible operation time resources. The investigations were based on the following assumption: If technical state degradation of gas turbine rotor sets is a function of their operation time ( at a load spectrum assumed constant) then it is possible to select from the recorded vibration signal spectrum such parameters whose changes can be unambiguously assigned to the operation lime. If critical values of the vibroacoustic parameters are known then it is possible to estimate a permissible operation time period on the basis of changes with time of the investigated parameters. During realisation of the investigations in question the use was made of producer's guidelines for permissible values of vibration parameters, as a initial comparative and verifying element. Because DR 76, DM 76 496
and DR 77 turbines are similar to each other an uniform measurement method was applied to all the considered engines (at observing individual values of symptoms).
REALISATION OF THE INVESTIGATIONS For realisation of the investigations the measurement instruments: GC-89 analyser and FFT-2148 analyser, of Bruel & Kjaer, were used making it possible to collect and process measured data. Measuring transducers (accelerometers) were fixed to steel cantilevers located on the flange of the lowpressure (LP) compressor only. It was decided to carry out the investigations with the use of the transducer fixed to the LP compressor flange for lack of transducers and equipment suitable for measuring signals at the temperature as high as 200°-^ 300° C occurring on the HP compressor flange. The fixing cantilevers characterised of a vibration resonance frequency value different enough from harmonic frequencies due to rotation speed of the turbine rotors. The measurements were taken perpendicularly to the rotation axis of the rotors. Such choice was made on the basis of theoretical consideration of excitations due to unbalanced shaft rotation, and results of preliminary investigations ofthe object [6]. As signals usable for the „defect-symptom" relation the following magnitudes were selected by the turbines' producer: Ysnc - 1^^ harmonic value of vibration velocity amplitude connected with the LP compressor , - Yswc - the same but connected with HP compressor - Yrms - root-mean-square value of vibration velocity amplitude within the range of 35 Hz ^ 400 Hz. The changes ofthe vibroacoustic symptoms were analysed in function of service time within the ranges: for DR 76 and DM 76 engines : from 0 to 2000 hours for DR 77 engines : from 0 to 1000 hours. The choice was justified by the time-between-repair values scheduled by the turbines' procedure. For purpose of these investigations a simplification was made consisted in assuming values of the afterrepair turbine vibroacoustic symptoms as those ofthe new turbine. To make such assumption was necessary due to rather low number of the investigated objects (only eight turbines of each type). The following limit values of rms vibration velocity amplitude were specified by the turbines' producer: for DR 76 and DM 76 engines : permissible value of Yrms equal to 24 [mm/s] , - permissible value of harmonics Y equal to 17 [mm/s] for DR 77 engines : - permissible value of Yrms equal to 30 [mm/s], - permissible value of harmonics Y equal to 20 [mm/s] For further diagnostic inference the criterial 1^^ harmonic values of HP compressor was rejected for the reason of an important influence of damping decrement on recorded values of Yswc signals. Determination of the maintenance time on the basis of a Yswc signal value is possible only indirectly by analysing Yrms and Ysnc signals.
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Results shown in Fig. during ship operation features, but also of expected maintenance turbines' procedure.
3 and 4 of the investigations of changes of the considered values of symptoms indicate that the maintenance time is a function of not only turbine design a selected exploitation policy. At the considered service load spectrum the time for both types of turbines was two times longer than that specified by the
As it was necessary to adjust operation procedures to warranty terms it was decided to establish twoway control of cleanness of the gas flow part of the turbines: 1^^ - by means of the endoscopic method and 2" - assessment of changes of the vibroacoustic parameters. The control is carried out at least two times a year for all gas turbine engines in service. Its scope also contains recording the values of the operational parameters whose changes could be an initial symptom of failures of the coupled devices as well as elements of the fuel supply system. All information is recorded and stored in the database of the system in operation. Results of the maintenance time assessment on the basis of Ysnc parameters for DR 77, DR 76 and DM 76 engines are presented in Fig. 2 and 3. Engine DR 77 N=1,2
signal Ysnc
E E
o oo oo oo o o o o oj i on o oO ' o « -o o T r r - -o o o co oo (o oD ao o) Co oN jo oi no oc oo oT -o o cocDcnr operation time
Fig. 2. Maintenance time assessed by means of Ysnc parameter Engines DR 76 & DM 76 N=1,0 signal Ysnc
operation time
Fig. 3. Maintenance time assessed by means of Ysnc parameter
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In order to obtain uniform diagnostic procedures regarding unbalance assessment of the turbine rotors the dimensionless parameters characterising that states were applied. On the basis of theoretical considerations as well as results of other diagnostic investigations carried out for some years the following parameters were selected as those most sensitive: 51 - ratio of the mean vibration velocity amplitude of a given rotor ( 1^^ harmonic) and the velocity component relevant to 2"^^ harmonic excitation frequency of the rotor in question 52 - ratio of the mean vibration velocity amplitude of a given rotor ( \^^ harmonic) and the velocity component relevant to 3*^^ harmonic excitation frequency of the rotor in question. From an analysis of the results the following minimum values of SI and S2 parameters were determined: - for DR 76 and DM 76 engines : SlSNC = minl.5 S2SNC = min 2.5 SlSWC-minl.5 S2SWC = min 2.5 - for DR 77 engine : SlSNC = minl.5 S2SNC = minl.8 SlSWC = minl.7 SlSWC-min2.9 where : SNC stands for LP compressor, SWC - for HP compressor. By analysing the kinematics system, the front internal bearing of the HP compressor rotor was selected as the most dynamically and thermally loaded one. By means of harmonic analysis of the vibration excitations connected with the bearing's work regarding the internal shaft unbalance it was possible to determine permissible values of the velocity amplitude VR of the vibrations characteristic for frequency difference of the rotor velocities of HP and LP compressors. They are as follows: r^ harm VR = 8 mm/s 2"''harmVR=1.6mm/s The presented method was verified by investigating also other parameters characterising technical state of the engine in function of operation time, such as skid, endoscopic control, starting parameters, lubricating oil contamination etc. Moreover, the permissible diagnostic parameter values specified by the producer were taken as those verifying the assumed vibration symptoms. The accelerometers were fixed in the same way as that assumed in the turbine producer's model of vibration energy propagation. Changes of values of SI and S2 parameters are presented in Fig. 4 and 5. COMMENTS TO RESULTS OF THE INVESTIGATIONS Two-way realisation of the investigations made reliable verification of the investigation results possible. The following detail conclusions were drawn for further diagnostic inference: • For DR 76 engines: Ysnc vibroacoustic parameters are diagnostically susceptible at the engine load N = 1.0, and/or DR 77 engines : Yrms and Ysnc parameters at the engine load N = 1.2. • Changes of 1 ^^ harmonic values connected with HP compressor rotors (Yswc) and LP ones (Ysnc) at the work of DR 76 and DR 77 engines at BJ load are hardly noticeable in function of operation time therefore their operational susceptibility is too low.
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Changes of values SI i S2 (SNC) DR 77 engines in functon of operation time
CM
6
C CO
. 4
•\
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I
E3
I
I
-S1 I
I
•S2
I 1 *0 operation time
Fig. 4. Changes of values of SI and S2 parameters in function of operation time for DR 77 engine
!
Changes of values S1 i S2 (SNC) DR 76engines in functon of operation time 98-
•
i
;: 6 ^ 5.
S2
I' S 3
• i"
^;
S 1
5 2 1n0
200 400 600 800 1000 1200 1400 1600 1800 2000 operation time
Fig. 5. Changes of values of SI and S2 parameters in function of operation time for DR 76 engine
•
Changes of Yrms parameter with operation time are not unambiguous hence it is of a low diagnostic merit. On this basis „symptom value - operation time" relationships were determined, and the time to next maintenance finally assessed. The engine load was assumed the criterion for estimation of the compulsory maintenance time for DR 76 and DM 76 engines, evolving from exceedance of permissible symptom values during normal operation of the engine. For the calculation of Y(t) values the factor k = 1.1 ( covering 10% measurement error) and the user confidence factor m = 1.05 was applied as follows : Y(t) = k • m • Yr(t) where : Yr(t) -vibration parameter function of operation time 500
For DR 77 engine Ysnc parameter is diagnosticaily susceptible because it leads to a shorter maintenance time at the considered maximum load. By taking into account the between-repair-time period for DR 77 engines amounting to 1000 hours the expected maintenance time of 2600 hours was assessed in accordance with its technical state on the basis of Ysnc parameter values (Fig. 2). DR 76 turbine engines are installed in the considered M - 15 E service propulsion system. The load N = 1.0 of them was assumed the criterion for determining their maintenance time basing on exceedance of permissible values of the considered symptoms at normal engine operation. For estimation of the engine's maintenance time according to its technical state such parameter was selected whose normal service changes determine the maintenance time shorter at a higher load. This was based on two following assumptions: • Forces connected with various unbalance forms, manifested in recorded vibration signal changes, increase along with rotational speed increase ( hence also with engine's load) • Resuhs of the investigations at the load N = 0,6 of DR 76 service engines and N = 0.8 of DR 77 peak-output engines have been rejected as the least credible ones. At these loads the engines operate within resonance speed ranges therefore the results could not be the basis for technical state assessment of the engines. Hence for estimation of their maintenance time, Ysnc parameter was selected and its value of 3150 hours was determined from exceedance of the permissible value (Fig. 3).
FINAL REMARKS AND CONCLUSIONS • • • • • •
Application of the proposed approach makes managing the engine's operation time much more rational, especially at its end. The proposed approach is non-invasive and does not require taking the ships out of service. Realisation of investigations of the kind makes it possible to collect data for a database of the future monitoring system of ships, expected to improve their operational features. Experience gained during the investigations would be utilised for other power plants equipped with gas turbines. The proposed diagnostic method is a coherent element of Basic Diagnostic System used by Polish Navy for many years. The proposed exploitation method leads to important economical profits and especially to reliability improvement, a first-rate problem.
From analysis of the presented results the following detail conclusions dealing with vibroacoustic investigations can be offered: - For further research the following target operation times of rotor systems (at the assumed load spectrum), required for their maintenance should be assumed : - 3150 hours - for DR 76 and DN 76 turbines - 2600 hours - for DR 77 turbines. - Assessment of the engine technical state by means of SI and S2 vibration parameters makes it possible to flexibly utilise engine operation time in the case of not performing repair operations. - Periodical control of Ysnc parameter trend development enables to credibly represent changes of a given parameter in function of operation time. - The Ysnc parameter was selected the same for both considered engines due to its unambiguous dependence on operation time and similar character of its changes. 501
Control of SI and S2 parameters during exploitation makes it possible to assess state of contamination of the gas flow part of the considered turbine engines, and exceedance of its permissible value could be taken as a signal for necessary washing of their compressor units. The proposed maintenance time resources concern only the rotor sets. Assessment (in the proposed time instant) of serviceability of the coupled devices, fuel supply and lubricating systems was not included into the scope of the present investigations.
REFERENCES 1. „Banek T., Batko W. : „Estymacja zaburzeh w systemach monitoruj^cych". Wyd. AGO, Krakow 1997 2. Charchalis A.: „System diagnozowania okr^towych uktadow nap?dowych z turbinowymi silnikami spalinowymi". Problemy eksploatacji, 27, 4'1997 3. Charchalis A.: „Diagnostyka okr^towych turbinowych silnikow spalinowych". Kongres diagnostyki technicznej '96, vol. I, 4. Charchalis A., Mironiuk W., Szubert J.: „Diagnozowanie okretowych turbinowych silnikow spalinowych na podstawie pomiaru drgah". Wyd. AMW, Gdynia, 1993 5. Downham E., Woods R.: „The rationale of monitoring vibration on rotating machinery" ASME Vibration Conference, Paper 71- Vib-96, September 1971 6. Grzadziela A.: „Ocena stanu technicznego ukiadu wirnikowego okretowych turbinowych silnikow spalinowych". XXVII Ogolnopolskie Sympozjum „Diagnostyka maszyn" ? 7. Lyon R., Deyong R.: „Design of a high-level diagnostic system". , vol. 106, January 1984 8. Meyer H.G.: „Reduction of shipboard vibrations". MER , November 1984.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
COMPUTER IMAGE ANALYSIS OF DYNAMIC PROCESSES E. Chrpova, L. Pfevratil and V. Hotaf Technical University of Liberec, 461 17 Liberec, Czech Republic
ABSTRACT Our research work focuses on the development and application of the means for computer image analysis of dynamic processes. The goal is to affect the quality of the products. The work was intended to develop a suitable software toolbox, complete with hardware equipment, measurements of production processes, and analyses of the measurements from a carding process, a weaving process, a paper production process and others. For the monitoring and obtaining parameters of analysis, we use three software tools - fractals, CIE colour space, and statistic measures. There are based on two distinct requirements for the algorithms. Firstly, they must be flexible enough to enable the best parameters for analysis to be selected and compared with other methods. Secondly, they must be fast enough for on-line control. Our investigation confirmed that many production processes could use the same principles of analysis in its processes. Our research shows the possibilities of application of three software tools in various processes. Our activity in this problem was supported by the project EU Inco Copernicus - Noviscam, Erbicl5CT960700. In this paper we attempt to clarify how changes in the production processes cause corresponding changes in the end products. Selected measurements are provided as means of documentation.
KEYWORDS Monitoring, quality control, image processing, signal processing, random processes.
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INTRODUCTION The work was intended to develop monitoring system for random processes based on video image during the production phase. The system consists of hardware equipment, data evaluation implemented in software and determination of acceptable tolerances related to final product quality. The research team has investigated applications of the methods in a paper production process, a carding process and a weaving process. The example of application to the carding process is shown in fig. 1. The captured processes are fast, they have chaotic properties and they are difficult to control with standard tools of process control.
Fibre web of production
Flock-fibres
^^m^^ output product carding machine
thermobonding Figure 1: Monitoring system
MONITORING SYSTEM Monitoring system is created by hardware equipment and by developed software tools.
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Hardware equipment 1. 3'CCD colour video camera JVCKY-FSSBE with variable focal lens Pickup device: 1/3-inch interlines CCDx3 Effective number of pixels: 440 000 pixels Colour separation optical system: F 1,4, RGB 3-colour separation prism S/A ratio: 58 dB Horizontal resolution: 750 TV lines (Y signal) 580 TV lines (RGB signal) Variable focal lens HZ - G6350U. 2. Digital Video Cassette Recorder DSR-20P System: DV Track pitch: 15iim Time mode: Drop frame system This recorder was purchased for simulation of the real production processes. This recorder cooperates with the measuring system. 5. Lightening Lightening reconmiended for digital video COMLITE 55W/12-950, 3 000 lumen with illumination by OSRAM DULUX L 2G11. 4, Accessories 2 beams and 4 lighting stands. One is for the camera and one is for the lightening. 5. Video and graphic system - dps Reality Computer: Intel PIII/450, SDRAM 128MB, ASUS P3B, AGP16 MB, SCSI UW, CD ROM 36speed, HDD SCSI Cheetah 18,2 GB, FDD 3,5", Industrial case 19". Dps Reality card provides the means for capturing images in real time. Dps Reality will work with any application that can read or write to any of the file formats supported by Virtual Tape File System, including SGI, BMP, PIC, TIF, IFF, VPB, RAS and RLA. Dps Realiy is simple interface with a trim table, timeline and preview windows designed with the compositor in mind. The heart of the system is tiie Reality card. Its on-board SCSI controller, designed to control Ultra Wide SCSI drives means that does not rely on the computer's bus to transfer video to disk. The video data is captured and stored in a 32-bit 4:2:2:4 MJPEG or uncompressed YUV format. The Reality card has a single input/output cable, which carries auxiliary, balanced and unbalanced audio, and component, composite and S-Video signals in both inputs and outputs. It provides analogue signal processing and character generator support. The two screens shown on right side of the window are waveform monitors. These waveform monitors have a vertical scale calibrated from -30 to + 130 units. (In NTSC this scale is in IRE units). For both NTSC and PAL a level of 100 corresponds to white. This is the correct level for brightest portions of the video signal. For PAL signals a level of 0 corresponds to black.
Software tools For the monitoring and obtaining parameters of analysis, the team uses three software tools that were developed in house named NOVISCAM technique (NOVIS, Video colorimeter, Statistics), shown on fig. 2. There are two distinct requirements for all used algorithms. Firstly, they must be flexible enough
505
to enable the best parameters for analysis to be selected and compared with other methods. Secondly, they must be fast enough for on-line control. Data can be analysed using various methods. The investigation considered new approach using fractal analysis in comparison with conventional statistical method as shown in fig. 3. The fractal analysis is described elsewhere. This paper uses results obtained on statistical bases (average value - x, standard deviation - s, coefficient of variation - v = s/x).
Statistical analysis
Fractal analysis
Video colorimeter X, s, V, tolerances
Box procedure
Rescaled range
Agregated variance
Figure 2: Analysis of data
MONITORING AND DATA ANALYSIS For the analysis a data record from CCD camera was used. Shots are transmitted to a computer as a data record and analysed. Images in a digital form are represented as matrix with values of pixels, and on the images windows are designated. Average values of pixels in the windows (fig.3) are read from images. The values are saved, and create time series. These time series are analysed by fractal analysis and statistical analysis. Single images with windows ^.^^
^^'^^ ^^^"^ of window 1 with the threshold
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.lr''M
R/S Dimension
i,h.
fii^^im I 'i^^:^-rJ
image
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Figure 3: Monitored image by fractal analysis
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Box Dimension
Fractal analysis The fractal analysis is based on evaluation of the time series, which were estimated by using "Iso-gray set", Rescaled Range Analysis (R/S) and "Box procedure" (fig.3). For this analysis was used software Matlab and the results are described elsewhere (1,2,3). Analysis by video colorinoieter Video colorimeter expresses the colour of measured flat object on-line (fig.4). This is the way, how to compare one colour to the next with accuracy. This analysis identifies a coloiu* expUcitly. That is, it differentiates a colour firom all others and assigns it a numeric value. This analysis enable to compare data obtainedfi-omthe most commonly used spectrophotometers. The image of an original picture is mostly projected through a set of appropriate optical filters and separated to three components of indirect trichromatic reproduction - red, green and blue (RGB). The optical signal generates electrical charge, large proportionally to the light flow, in 2D matrix of sensors of video camera, every element of CCD field behaving as a micro-photometer. The charge is shifted in rows and sequentially read out and matrixes from RGB representation to YUY-video devices native colour space. The video signal from camera is fed to an analogue-to-digital converter. The digitised representation, also known as Y,Cb,Cr according to ITU R.601, is stored in a frame buffer and analysed by software in real time. It is conveyed to the production process control system via TCP/IP network then. Brightness (Y)
Width of monitoredflat product
Measured data: CIEXYZ X Y Z CIExyY X Y Y CIELah L* a* b* Figure 4: Monitoring and data analysis by video colorimeter
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The averaged values of Y,Cb,Cr components are transformed to CIE 1931 XYZ, trichromatic components x,y,Y components, as well as to CIE 1976 Lab components. The lab colour model has been chosen as an optimum in the given application.
Statistical analysis This analysis is devoted for measurement of Non-uniformity of the flat fabrics. The program processes a video-signal, which was originally received by the dpsReality video-card and passed to the program in a suitable digital form. The processing uses statistical analysis (fig. 5) of the data to determine deviation from a uniform condition in running mode statistically. While the 'ruiming' mode employs real time processing and analyses continuous flow of frames, the static processing is applied to selected single frames and allows more detail analysis. Y,Cr,Cb i i upper X
1
/W\/\AA/\ff
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lower variation in width Figure 5: Principles of data analysis
*Running* mode In this mode the computer divides every frame into a selected number of vertical stripes (stripes in the direction of material moving under the camera). The mean value of brightness and that of two colour components are then calculated for each stripe. These values are then plotted in graphs so that all three-colour components can be monitored in time. Depending on the application of the system these components represent for example the density of raw material for carding, density of fabric etc. for the whole monitored width. The program also determines the value across the whole width. The values are plotted so that the operator can monitor the changes in signal intensity and colour in time. At the same time the program determines standard deviation, minimum and maximum value and deviation of the frame values from the mean value. The program also checks if the calculated values are within the specified tolerances (one for each frame component). Cases over the tolerance limits are recorded and reported. It is possible that the recorded parameters either gradually drift or suddenly change to different levels. In such cases a new central value of the tolerance field can be selected even during the run of the program using a specific key on the control panel. Static mode In this mode the program analyses individual frames selected by the 'collect frame' command. On this command the last frame is transferred from current memory and statistically processed. The frame is divided into a specified number of vertical and horizontal stripes. For each such generated oblong the program calculates the mean value of colour components, minimum and maximum values and
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standard deviations across the wholeframe.CUck of a mouse on a specific part of theframeprovides values at this point. A 'survey' window is also a part of the program. This window shows the picture recorded by the video camera in real time and allows of the feed rate of the recorded material. The value of this feed rate is important for determination of the overlap of individualframes.This overlap must be eliminated from processing so that the processed sections form a continuous image corresponding to monitored material and no data are processed twice. TABLE 1 Datafromstatistical analysis in "running" mode Sample of flat products 1 - fibrous web - good quality 2 - fibrous web - worse quality 3 - fibrous web - bad quality 3a - bad quality of fibrous web 4 - paper (bad quality) 5 - paper (good quality) 6 - woven fabric on M 8300 (quaUty) 7 - woven fabric on inspection frame 8-fibrous web from 1 production line 9 - fibrous web from 1 production line
Record on Mean value Maximum 1 Maximum relative video tape of brightness relative increase reduction 7,4 % 4,7 % 28:51 to 123 29:31 6% 6,7 % 29:34 to 127 30:14 25,1 % 32,4 % 30:18 to 132 31:27 8,6 % 11,2% 30:18 to 135 30:35 0,8 % 1,7% 32:55 to 146 33:53 0,5 % 0,7 % 33:56 to 146 34:56 0,8 % 0,8 % 37:17 to 133 38:15 0,8 % 38:18 to 135 1,1 % 39:16 5,6 % 6,1 % 42:24 to 165 43:22 6,2% 7,2 % 143:24 to 167 44:24
Relative standard deviation 2,3 %
Time 1 frame high 1,5 s
2,4 %
1,5 s
8,9
% 1,5 s
3,2%
1,5 s
0,4 %
1,5 s
0,2 %
1,5 s
0,2 %
12 s
0,3 %
0,6 s
2,2 %
0,2 s
2,4%
0,7 s
Notes: real feed rate = height offield/timefor passing the height; brightness values from 15(black) to 255(white). Technical parameters The video-signal contains 25 frames per second. In order to process the signal in real time the time for processing single frame data cannot be longer than 40 ms. This is what is happening in the described case. The speed of recording also limits the feed rate of progress of the recorded material. Specifically the feed rate must be lower than the height of the recorded field multiplied by the recording frequency. Under normal conditions with the height of the recorded field 40 cm the feed rate of the recorded material cannot exceed 10 m/s. This is a very high value which is generally not achieved in production.
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The resolution of the recording is also important and can be a limited factor in some potential applications. What comes to mind is the analysis of the quality of woven fabric, where is a need to detect missing wefts and similar defects. The recorded field of a camera typically contains 720 * 576 pixels. For a required resolution of 2 pixels per mm the camera can record less than 40 cm of material width.
APPLICATIONS This project investigated three applications: the paper production process, carding process and weaving process. Paper production The basic procedure of the paper production process is as follows: the raw materials are slashed of with water to obtain a homogeneous suspension. The suspension is distributed on to a wire screen that is similar to a conveyor belt, and water is filtered off Vacuum pumps drain some of the remaining water. The paper web is then compacted and is pressed between rolls. The last part of the production is drying on hot cylinders. Carding process The carding process produces a fibrous web (flat layer of fibres) from flock of fibres and the web is input material for production of threads which are the basic material of flat fabrics as are for example weaving and knitting fabric. The fibrous web can be also used as basic flat material for non-woven textiles. The uniformity of the flat layer of fibres is main parameter important for textile process. Weaving process The weaving process uses 2 systems of threads (weft and warp). Woven fabrics are made on looms by weaving. The measurement was made on multiphase weaving machine M 8300. The measurements were conducted at an inspection frame and a weaving machine. The image record contains good and poor quality. The image data record is currently being analysed.
DISCUSSION Results from the carding process, the paper production process and weaving process show the possibility of using statistical method, appHed brightness and colour signals from video camera. It seams that the use of statistical methods provides an alternative to fractal analysis. Our investigation opens the way for process control, which will reduce the number of defects and will result in high quality of the product.
ACKNOWLEDGEMENTS The investigation has been carried out in the framework of EU Inco-Copernicus programme in collaboration with the University of Hertfordshire, England, Turboinstitut, Slovenia, the University of Bremen, Germany, BIRAL, England and FILPAP, Czech Republic.
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CONCLUSION 1. The system for monitoring random production processes has been developed. 2. The system is based on video image and suitable signal processing. 3. The system has been applied to paper and textile production processes.
REFERENCES Hotar,V., Chrpova,E-5 Lang,M, Philpott,D. (2000). Application of Fractal Dimension in Carding, Paper and Other production processes. Final Report of Sub Team - CeVis (University of Bremen), Technical University of Liberec. Chrpova,E., Hotaf,V., Lang,M. (2000). Application of "NOVISCAM TECHNIQUE" and Fractal, Dimension in Paper, Glass and Textile Production Processes.lSQW?VD'2000, Bled, Slovenia. ISBN 961-6238-38-8, 86-98. Chrpova,E., Hotaf,V. (2000). Application of Fractal Dimension in Textile Production Processes. The Textile Institute, 80th World Conference, Manchester, England. ISBN 187037245X.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
INVERSE METHOD OF PROCESSING MOTION BLUR FOR VIBRATION MONITORING OF TURBINE BLADE
Tadao KAWAl', Masami ITO^ Yoko SAWA^ and Yasuhiro TAKANO^ ' Associate Professor, Department of Mechanical Engineering, Nagoya University Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan ^ Engineer, Production Engineering Laboratory, Matsushita Electric Co. Ltd. ^ Master course student, Department of Mechanical Engineering, Nagoya University ^ Engineer, Toyota Motor Co.
ABSTRACT Among vibrations to occur in a turbine, vibrations of a main shaft or a blade are very dangerous. A lot of systems monitoring vibrations to
occur to a main shaft were developed, and they have been
commercialized. On the other hand, it is very difficult to measure vibrations of a turbine blade during operation because blades are united with a main shaft and turn. Accordingly, the method to estimate vibration of the blade is limited to measure vibration of a blade at rest or to analyze it by the finite element method. If it become possible to measure vibration of a blade in an operation state, a designer can design the more reliable and reasonable turbine and an operator can maintain the turbine system more safely. In this study, we propose the technique to measure vibration of a turbine blade in a turn using an image processing. By using the CCD camera, we capture the end face of a blade in a turn and determine a position of a blade from an image data, and then estimate amplitude of vibration statistically. The motion of the blade at high speed makes a captured image blur and degrades the estimation of the position of the blade. By processing the blurred image as the inverse problem, we can estimate the position of a blade precisely. One piece of image data is provided by a turn of once. Because natural frequencies of a blade are higher than the rotational speed, we are requested to plot the probability density of the position of the blade for estimating amplitude of the vibration. Under a supposition that the blade vibrates sinusoidally, the probability density for the blade vibration can be calculated. By comparing the probability density for sine wave with the processed probability density
513
for the blade, the amplitude of blade vibration can be evaluated. The rotating speed of the blade and the shape of an end face of a blade affect the estimation of the position of the blade. We check these effects and verify the high accuracy of our technique experimentally.
KEYWORDS Turbine blade, Vibration, Motion Blur, Image Processing, Inverse Problem, Statistical Method 1 INTRODUCTION Recently, the techniques to reduce the total cost and to support the machine life become more and more important in the engineering. Because the production skills are well developed and the production costs are reduced extremely, many companies turn their attention to support their productions.
Instead of building a new plant, it is strongly required to let all kinds of system
including turbine systems work for a long term because of the severe economic situation. On the other hand, a use of a long term damages a machine and harms its reliability. This is why the reliable monitoring systems are required to check the conditions of the system and its damage. Many diagnosis systems to check the rotor vibrations of a turbine have been developed ^'^^-^-^^ and, actually, works at a lot of facility. On the other hand, there is not a useful means to measure the vibration of the turbine blade at a real operation state because it rotates around the main shaft of the turbine. On this account, the techniques to predict fatigue life of a joint of a blade are limited to a measurement of a vibration mode of a blade by a standstill state or the FEM analysis.
If the
technique to measure the vibration of the blade directly in a real operation state is developed, the more reliable design of the turbine and its work are guaranteed. Image processing has a number of attractive features; e.g., it is the non-contact measuring method and many size of the object is measurable by changing its lens system. In this study, we capture the end face of a turbine blade using the image measurement device installed in turbine case and estimate amplitude of the vibration of the blade in its operation state. Because the rotational speed of the turbine is extremely high, the captured image of the end of the blade is blurred.
In addition, the round end face of the blade and the non-uniform light intensity
make the estimation of the blade vibration difficult. We overcome problems mentioned above using approach of inverse problems and developed the system for measuring the vibration of turbine blade with high accuracy. 2 ANALYTICAL MODEL In this study, we develop the technique to measure amplitude of the blade vibration by processing the captured image of the end face of the blade using a CCD camera. Under the supposition of the turbine blade to vibrate with the first mode only in rotary direction by analysis, we treat the image data as one dimension about rotational direction.
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1 pixel of object ^ 1 2
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Fig. 1 Measuring System Blurred image: b
Fig. 2 Process of making blur image Figure 1 shows a conception diagram of a measurement system. In the case of capturing an image of the moving object with finite time, the image blurs ^"^^^^l In addition, an image is digitized and quantized into pixels by CCD. In the following analysis, the matrix having the rotational speed of the blade v and the opening period of the camera At expresses the motion blur. At first, imagine that the end face of the blade is located in one flame of an image data. The frame is expressed by the one-dimensional vector of which size is M. Also, the gray level of the background is set to be zero. Equation 1 relates the exact image data x to the blurred image data b. Ax = b
(1)
where A is a M by M matrix, x and b being M vectors. The matrix A is a matrix to blur an original image x (in the following secfion, we named A as the blur matrix). Figure 2 illustrates the process of making a blur matrix. At first, divide the duration of a photographic exposure At into A^ sessions imaginary. One pixel of object image moves v At /A/^ during the session of At IN (see white block in Fig.2). Also during the session, each photoelectric conversion element of a CCD device gets the light energy (see hatched block in Fig.2). After the light energy is accumulated during the whole duration of a photographic exposure of At, each element of a CCD gives output voltage. By applying above idea to the whole object image, the blur matrix is constructed. 3 ANALYSIS METHOD 3.1 Reconstruction of image Because it is very difficult to check the vibration of all blades, we develop the basic method to
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measure anplitude of only one blade as the first step in this study. We capture an image of end face of the blade once at a rotation. If a blade does not vibrate, a position of a blade is always the same. However, a position of a blade changes according to the vibration amplitude of the blade at the moment of capturing image. Usually, the natural frequency of the blade is extremely higher than the rotational speed of the shaft. So, amplitude of blade vibration cannot be determined directly according to position data given once at a rotation. In this study, we gather a lot of position data of blade and process statically to get amplitude of the blade vibration. At first, the method of measuring the precise location of blade by processing the blur image of the end face of blade is explained. Theoretically, a location of the blade is estimated by restoring a blurred image data. Here, the position of the blade was defined as the position of the left end of the blade. x = A-'b
(2)
On the contrary, various noises in a captured image data make the equation 2 ill posed, and give bad reconstructed image data. In this report, we use an inverse matrix depending on the singular value decomposition to regulate the inverse problem as described in section 4.3. 3.2 Estimation of amplitude by statistical technique Generally speaking, first natural vibration is easily induced and gives important effects to the system. In this report, we assume the case that the blade vibrates with its first mode in the rotary direction and develop the method to measure its amplitude. In the assumption of the blade vibrating sinusoidally with an constant amplitude, we can estimate its probability distribution by equation 3^^^ [(x^X'-x'V p{x)^\\ J [O
\x\<X >I \x\>X
(3)
where X is the amplitude of vibration.
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Fig.3 Histogram of estimated positions
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As mentioned above, one position data is given by processing one image data. By repeating this process many times, the histogram of blade position is illustrated. According to the assumption of sinusoidal vibration, we can estimate amplitude of vibration by matching the theoretical distribution function to the experimental histogram in Equation 3. Here, the amplitude is determined at the condition that the correlation between both data has the maximum value. Figure 3 shows the experimentally measured histogram with probability distribution curve determined by the above criteria. 4 THE PROBLEMS IN POSITION ESTIMATION AND ITS IMPROVEMENT 4.1 Background data In Chapter 3, the background data is assumed to be zero. However, in the experiment, a background in the captured image data gives important effects on estimating the location of the blade. These are a degradation of gray level and the border distortion. Lightning at the experiment and capturing an image at high degree of gray level improve the effect of a degradation of gray level. rAw^-Av/v^^'^-'-vV
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Fig. 4-Network training and prediction for Throttle position data 534
60
It can be seen that the network produces good prediction results for training and reconstructing the data of missed parameters using data from other related parameters. Figures (5) to (7) present the results of the error analysis for data reconstruction for each sensor. The error in speed data reconstruction, which is the difference between the original data and the reconstructed data, has an average value of 14.6 RPM. This means that we can estimate the engine speed to within 15 RPM, which is acceptable The throttle position sensor, which is a potentiometer, has the lowest error 0.001015 Volt due to it being less sensitive to noise and interference. Network Test Error
20 30 40 Measurement samples
Fig. 5- Error analysis for engine speed data reconstruction X 10
Network Test Error
20 30 40 Measurement samples
Fig. 6- Error analysis for manifold pressure data reconstruction 535
Networtc Test Error
X 10
10
20 30 40 Measurement samples
Fig. 7- Error analysis for throttle position data reconstruction
CONCLUSIONS AND FUTURE WORK Fault accommodation by parameter estimation using neural networks has been demonstrated. Good results have been obtained which give motivation to apply it on-line. Neural networks may also be used for fault detection and isolation. Future work will involve the investigation of these capabilities to achieve a fault tolerant sensor system using only one technique.
REFERENCES B. Johnson. (1989). Design and Analysis of Fault-Tolerant Digital Systems. Addison-Wesley Publishing Company H. Demouthe & M. Beale. (1997). Neural Network Toolbox User's Guide. Mathworks P. D. Wasserman. (1993). Advanced Methods in Neural Computing, Van Nostrand Reinhold S. Simani & C. Fantuzzi. (2000). Fauh Diagnosis in Power Plant using Neural Network. Information Science. 121:2000, \25-\36.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
THE APPLICATION OF NEURAL NETWORKS TO VIBRATIONAL DIAGNOSTICS FOR MULTIPLE FAULT CONDITIONS A J . Hoffinan\ N.T, van der Merwe\ P. S. Heyns^ C. Scheffer^ and C. Stander^ ^School for Electrical and Electronic Engineering, Potchefstroom University for CHE Potchefstroom, 2520, South Africa ^Department of Mechanical Engineering, University of Pretoria Pretoria, 0002, South Africa
ABSTRACT Vibration analysis has long been used for the detection and identification of machine fault conditions. The specific characteristics of the vibration spectrum that are associated with common fault conditions are quite well known, e.g. the BPOR spectral component reflecting bearing defects and the peak at the rotational frequency in the vibration spectrum indicating the degree of imbalance. The typical use of these features would be to determine when a machine should be taken out of operation in the presence of deteriorating fault conditions. Reliable diagnostics of deteriorating conditions may however be more problematic in the presence of simultaneous fault conditions. This paper demonstrates that the presence of a bearing defect makes it impossible to determine the degree of imbalance based on a single vibration feature, e.g. the peak at rotational frequency. In such a case it is necessary to employ diagnostic techniques that are suited to the parallel processing of multiple features. Neural networks are the best known technique to approach such a problem. The paper demonstrates that a neural classifier using the X and Y components of both the peak at rotational frequency and the peak at BPOR frequency as input features, can reUably diagnose the presence of bearing defect and can at the same time indicate the degree of imbalance. Different supervised and unsupervised neural classification techniques are then evaluated for their ability to reliably model the degree of imbalance, while also identifying the presence of defects.
KEYWORDS Vibration analysis, bearing defects, imbalance, deteriorating fault conditions, automated diagnostics, neural networks, Kohonen feature maps, nearest neighbour rule, radial basis function networks.
537
1. INTRODUCTION In a system with multiple fault conditions present, such as bearing defects, unbalance and looseness, it is important to distinguish between the fauh conditions. A situation may arise where certain fault conditions are stable while the conventional analytical procedures indicate degradation of the fault. This phenomenon occurs due to the presence of other fault conditions that are deteriorating. The objective of this research was to develop a condition monitoring strategy which can make accurate and reliable assessment of the presence of specific fault conditions, and which can furthermore distinguish between deteriorating and stable faults in a system with multiple fault conditions present. The proposed strategy involves the identification of fault conditions by vibration analysis, incorporating neural networks to either model the status of fault conditions, or to discriminate between different fault conditions. Vibration measurements were taken on a simple test rig that is subjected to increasing imbalance in the presence of a bearing defect. Various investigations into the detection of multiple fault conditions using vibration monitoring has been conducted by other researchers (McCormick & Nandi 1997, Paya 1997). No specific mention could, however, be found of a bearing defect in conjunction with an imbalance condition. The focus of this article is to demonstrate a strategy for the recognition of multiple fault conditions, applied specifically to the above mentioned fault condition. 2. EXPERIMENTAL SETUP Multiple fault conditions were induced on a Vib demo VIB 2.100 Pruflechnik AG test rig. The test rig consisted of three plumber block bearings, which support a shaft. An outer race defect was induced on the centre bearing of the test rig. Residual imbalance was induced on to the shaft of the system, using weigths of 0, 12, 18 and 24 grams respectively, to simulate a deteriorating secondary fault condition. Acceleration measurements were taken on the centre bearing and on the bearing closest to the induced imbalance. Vibration measurements were taken with 100 mV/g ICP accelerometers in both the horizontal and vertical directions during the four tests. A DSP Siglab analyser model 20-42 was used to collect the data. The rotational speed of the system at 1626 rpm was measured with an ONO SOKKIHT-4100 digital tachometer. The frequency band was set between 5 Hz and 10 kHz for the analysis. 3. FEATURE SELECTION AND EXTRACTION The vibration signals form a multivariate feature space. The required number of training samples for a classifier generally increases exponentially as a function of the number of features, assuming uncorrelated data (Hoffinan & Tollig, 1998). Furthermore, the performance of the classifier is closely linked to the quality of the features. The extraction of a compact feature set, which can still capture most of the correlation inherent in the original sample space, is thus very important in a multivariate setting. Suitable feature extraction methods highlight the important discriminating characteristics of the data, while simultaneously ignoring the irrelevant attributes (i.e. noise).
538
Figure 1: Test rig The frequency domain provides a useful feature set for machine diagnostics (Rao, 1996). Machinery defects are related to specific frequency domain features (Norton 1989, Rao 1996). It is well suited to the detection of periodical machinery vibrations. Impulsive vibrations, on the other hand, are better analysed in the time domain. Envelope spectra analysis is a technique especially suitable for the early detection of damage to rolling element bearings. The technique essentially consists of a bandpass filter that reduces components unrelated to the bearing. Envelope detection of the signal is then performed by full-wave rectification and low-pass filtering, after which spectrum analysis can be applied (Norton, 1989). It is well known that defects in rotating machinery can be monitored with vibration frequency domain analysis. The frequency spectra of a bearing, with increasing imbalance being imposed by adding weights to the setup, are shown in figure 2. For this paper specific features from frequency domain analysis were extracted to predict multiple faults in the experimental setup. These features can be calculated using common condition monitoring techniques. For frequency domain analysis, the frequency band of 5 Hz to 10 kHz was investigated. Time signals were recorded at a sampling rate of 25.6 kHz. For spectrum calculation, typically 20 processing averages v^th a resolution of 1 - 2 Hz were used. The following features were extracted from frequency domain analysis: • Amplitude of vibration spectrum at Rotational Frequency (RF), in horizontal and vertical directions. • Ball Pass Frequency on the Outer race (BPFO) of the defect bearing, in horizontal and vertical directions. • Higher Frequency Domain components (HFDs) indicative of bearing defect. • Ball Pass Frequency on the Outer race (BPFO) of the defect bearing, obtained from the envelope spectra. The amplitude at RF is commonly used to detect imbalance, while the BPFO component is indicative of a defective bearing. Should both fault mechanisms however be present, no single feature can completely distinguish between the different fault categories. This is illustrated in figure 3.
539
¥ - - r — r - ^ ^ ^ 100
200
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Figure 2 : Increasing imbalance on channel 1 (0 ~ 1 kHz) Bemmg d«fed Normal defect x Imbalancr. Og k I2g g I8g b 24g r
Figure 3 : Scatterplot of rotational frequency X and Y components 4. DEVELOPMENT OF CLASSIFIERS Several different approaches for modelling class membership in a data set with ANN are possible. Firstly one can work with afixednumber of known classes (typically identified through an exploratory analysis using SOM techniques). Alternatively one can start with a reduced classification problem, with each class representing a well defined fault condition, and then increase the number of classes as additional data becomes available. Secondly one can either indicate class membership by an associated output value of 1 for the corresponding ANN output (whereas a 0 indicates the opposite), or one can use the outputs of the ANN to indicate, on a continuous scale, the severity of the presence of a condition. Classifiers were trained to distinguish between the following six classes: • Imbalance masses of Og, 12g and 24g (with no bearing defect) - classes 1 to 3. • Imbalance masses of Og, 12g and 24g (with an outer race bearing defect) - classes 4 to 6.
540
4.1,
Kohonen self-organising maps
The well known Kohonen self-organising map (SOM) (Kohonen, 1998) was firstly employed. After normalising the features identified in section 3, a feature matrix must be prepared to be used as input to the SOM. The trained SOM, displaying all four features are shown in figure 4. The relationships between the features can be deducted from this figure. It is clear that the two features indicating unbalance is low towards the left hand side of the map, and increase towards the right hand side. The features indicative of bearing defect display a different behaviour. These features divide the map in two distinct regions, lower (high values) and upper (high values). With these relationships in mind, the Best Matching Unit (BMU) labels of training data are plotted onto the map, shown in fig. 5 (left hand side). It can be seen from fig. 5 that eight regions are formed on the map. The lower half of the map are BMUs indicative of a defect bearing. The top half is for the normal bearing. Furthermore, unbalance increase from left to right. Hence, the SOM can distinguish between the normal and defect bearings with certain levels of unbalance present. The right hand figure displays the BMU trajectory for the training data. From this, it can be seen how the BMUs are found as the training of the SOM commence through the data set as arranged in fig. 5. RF vertical (27 1 Hz)
RF horizontal (27 1Hz)
11
10.8
1o.6 10.4
10.2
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Figure 4
SOM displaying all four features
RF vertical (.'7 iHz)
Figure 5
Class labels and Best Matching Unit trajectory on SOM
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4,2.
Nearest neighbour rule (NNR) classifiers
The one nearest neighbour rule (abbreviated as NNR) has been successfully applied in the past to a variety of real world classification problems. As the NNR effectively stores or memorises the training set, the computational resources required can become a problem with large data sets (e.g. in data mining appHcations). We use a technique which allows continuous class membership values, instead of the binary class labels usually associated with the NNR. While the NNR selects the closest sample, irrespective of the class label, our approach is to select the closest class (or cluster) to the sample. The output value of this nearest neighbour rule with class membership (NNRC) can vary continuously, while the NNR always assigns a discrete class label (based on the distance to the closest neighbour). This enables the NNRC to be appUed in the modelling of a primary fault mechanism (an outer race defect) in the presence of a secondary fault mechanism (an imbalance mass) (Van der Merwe et al, 2001). The advantage of NNRC relative to NNR in classification problems would be that a degree of class membership can be obtained, as is possible for example in FLNN (fiizzy logic neural networks). The advantage of the NNRC over other modelling techniques, such as neural networks (multilayer perceptrons or radial basis fixnctions), is that no lengthy training time is necessary. Figure 2 shows the output of a NNRC classifier trained on observations fi-om all six classes. The BPFO feature indicating a bearing defect condition increases firom front to back, while the RF feature indicating imbalance condition increases form left to right. The data for the NNRC technique has been normalised with respect to length (norm of one).
Figure 6 : Response of NNRC classifier trained on aU 6 classes (training samples shown in black)
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4.3.
Radial basis function (RBF) networks
RBF classifiers normally perform well for classification problems with well-defined clustering in the data set. Such clustering could be achieved for this problem by utilizing the appropriate combination of features. Figure 7 shows the output of a RBF classifier trained on observations from all six classes. The BPFO feature indicating a bearing defect condition increases from fi"ont to back, while the RF feature indicating imbalance condition increases form left to right. The abiUty of this network to generahze was tested by feeding it with unseen Og, 12g, 18g and 24g data, measured on bearings with and without defects. It achieved 100% accuracy on the Og, 12g and 24g data. The 18g data was classified as either 12g or 24g, with the presence of bearing defect always handled correctly. It would hence be successful in classifying unseen inputs as having either very little, moderate or severe imbalance, as well as identify the presence of a defect. A network trained on the data for the normal bearing only, could however not correctly distinguish between the 12g and 24g classes for data collected from a defective bearing. This illustrates the necessity to use as representative a training set as possible in training such a neural classifier.
Outpuls of RBF network for own training data
iefect - BPOR x channel Imbalance - RF x channel
Figure 7 : Outputs of RBF classifier trained on all 6 classes (training samples shown in black)
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5. CONCLUSION Three different neural classification techniques were evaluated for their performance on a condition monitoring problem requiring the identification of multiple fault mechanisms. The SOM proved to be an easy to use tool for initial data processing and for identifying hidden relationships between the features. It also requires very little human intervention. It can be concluded that the SOM can be used for identification of multiple faults if the features are chosen and normalised correctly and if the class labels of the training data are known. It was shown that, once the classification problems is well defined, both NNR and RBF classifiers can accurately discriminate between different combinations of multiple fault conditions, as well as identify the degree of severity of deteriorating fault conditions. It was demonstrated that incomplete training sets will lead to faulty diagnostic decisions. RBF classifiers potentially have an advantage above SOM classifiers in terms of training speed. NNR techniques have the advantage of not requiring any optimization during training, that can reduce the computational requirements. Once trained, RBF classifiers however have a speed advantage over NNR techniques for the evaluation of new samples, which may be a problem for NNR classifiers in the case of large training sets. REFERENCES Hoffinan A. J. ToUig C. J.A. (1998). Neural network recognition of partial discharge signals. South Afiican Power Engineering Conference. Cape Town. Kohonen T. (1998). The self-organizing map. Neurocomputing. 21, 1-6 Paya B.A., Esat I.I. and Badi M.N.M. (1997). Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical systems and signal processing. 11:5.751 -765. Haykin S. (1994). Neural networks: A comprehensive foundation, Macmillan publishing company. Norton M.P. (1989). Fundamentals of noise and vibration analysis for engineers, Cambridge University Press. Rao B.K.N. (1996). Handbook of condition monitoring, Elsevier Science. Koncar N. (1997). Optimization methodologies for direct inverse neurocontrol. Master's thesis. University of London. Van der Merwe N.T., Hoffinan A.J., Stander C , Scheffer C , and Heyns S.P. (2001). Identifying multiple faults in rotating machinery, South Afiican Power Engineering Conference, Cape Town.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
APPLYING NEURAL NETWORKS TO INTELLIGENT CONDITION MONITORING W Li \ R M Parkin \ J Coy ^ A.D. Ball \ F. Gu ^ ^ Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LEI 1 1 AH, UK ^ Technology Centre, Consignia, Swindon, SN3 4RD, UK ^ Manchester School of Engineering, University of Manchester, Manchester, Ml3 9PL, UK
ABSTRACT In this paper we present the application of self-organising map (SOM) networks to the study of machine intelligent condition monitoring. The SOM networks are able to classify machine conditions with little priori knowledge. The condition-indicating information is condensed into the network's synaptic weights which can be used for further analysis. The paper is organised as follows. Firstly basics of SOM networks are discussed with emphasis on input vector distribution approximation. Secondly, the applications of SOM networks are verified with numerical examples. Thirdly, the SOM networks are applied to approximate the distribution of machine generated acoustic signals. Spectral information is extracted from network synaptic weights and used to compare different conditions. The spectral interpretation of the SOM synaptic weights provides an efficient and novel analysis technique. KEYWORDS Self-organising map, condition monitoring.
INTRODUCTION The condition monitoring of diesel engines employs a variety of techniques such as vibration analysis, acoustic analysis, thermography, combustion pressure analysis and instant speed analysis (Priede 1980). The signal processing methods range from time domain statistical parameters to the complicated joint time-frequency analysis (Li 2000). One of the problems encountered with the applications of these individual signal processing methods is that a diesel engine is a far more complicated dynamic system than bearings and gearboxes. Hence it is not always possible to classify the non-linear characteristics of diesel engine conditions with the above
545
linearly based signal processing methods. From the viewpoint of engine condition monitoring, it is more desirable to apply the non-linear pattern recognition and classification strategies in order to analyse engine conditions. It has been noted that neural networks are able to approximate almost any non-linear functions (Schalkoff 1997). This provides the fundamental fact that it is possible to model the non-linear dynamics of diesel engines from their outputs. The applications of neural networks to machine condition monitoring and fault diagnosis have been reported recently. Paya et al. (1997) investigated the condition monitoring of rotating machinery using backpropagation (BP) neural networks. The discrete wavelet transform was first applied to pre-process the vibration data before feeding them to the neural network. Both signal and multiple faults were successfully detected and classified. Li et al. (2000) adopted some of the common normalised indices such as peak-peak, mean, Kurtosis, and bearing characteristic frequencies to train a feedforward network in an effort to diagnose motor and rolling bearing faults. The model was verified with both the simulated and real time vibration data. Zhang, et al (1996) applied the SOM to study the bearing related machinery faults in which the normalised and dimensionless vibration data in the range of [0.0, 0.1] were fed to the network for classification. One of the first applications of neural networks in engine fault diagnosis was put forward by Scaife et al (1993). They applied neural networks to identify engine component failures. Thompson et al (2000) set up a neural network based engine model in predicting diesel engine exhaust emissions. The advantage of the neural network model is that the knowledge of engine performance governing equations and the combustion kinetics of emissions formation is not required. However, it can be seen from the above references that mostly vibration and emission related data are used in neural networks for classification. On the other hand, although acoustic signals were found containing very useful information about diesel engine conditions, they have not been investigated by neural network approaches (Li 2000). This paper will investigate the engine generated acoustic signals using the SOM. A novel interpretation method is proposed in analysing network's synaptic weights. SELF-ORGANISING MAPS This section briefly introduces the foundations of self-organising maps (SOMs) to be used in this paper. A self-organising map (SOM), also known as topology-preserving map, is formed by a topological structure of input patterns. In this map the spatial locations of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. Usually, the map is arranged in a one- or two-dimensional plot yet higher dimensions are not commonly used due to representation difficulties. The architectures of the SOM network have two basic types: the Kohonen model and the Willshawvon der Malsburg*s model as shown in Figure 1 (Haykin 1999). It can be seen that both models arrange output neurons in a two-dimensional lattice structure.
546
Winning neuron
Output neurons
Input neurons /
Input
O O O • O O O O
(b)
(a)
Figure 1: Self-organising map architectures (a) Kohonen model, (b) Willshaw and von der Malsburg model The Kohonen model, proposed by Kohonen (1982), outlines a topological mapping which optimally projects a fixed number of vectors into a higher dimensional input space and is suitable for data compression. The second model, jointly introduced by Willshaw and von der Malsburg (1976), is composed of two separate two-dimensional lattices of neurons connected together. The input neurons are projected onto the output lattice but unlike the Kohonen model, this model has strong biological grounds in that it is trying to explain the mapping relationship between the retina and the visual cortex. The purpose of the self-organising map (SOM) is to project the input patterns onto a one- or twodimensional map so that the output neurons are arranged in a similar manner as that of the input space. The learning algorithm of SOM can be stated as follows: Step I: Initialisation Like any other neural network the synaptic weights connecting the input layer and hidden or output layer are initialised first. One common way of initialising the weights is to assign each weight with a small random value. Step 2: Competition Given an input pattern, the network computes the values according to a chosen discriminant function. Among the output neurons, only one particular neuron with the closest relationship to the input vector is picked up and labelled as the winning neuron. Suppose an input vector picked randomly from an input space is denoted by
0) where m is the dimension of the input space. The synaptic weight vector connecting the J th neuron and the input vector is denoted by
547
Wy =K,,%2.---.v^y>„F»
j = l2,"-,n
(2)
where n is the total number of neurons. To determine the closeness between the input and weight vectors, the Euclidean distance is often used so that the winning neuron is picked up according to ||x-w,.|| = min||x-w^.||,
7 = l,2,---,«
(3)
hence the /th neuron is picked up as the competitive neuron. This winning neuron best matches the input vector in the sense of Euclidean distance. Step 3: Cooperation Once the winning neuron is picked up, the centre of a topological neighbourhood for co-operation is determined. The next step is to select the neurons within the neighbourhood and there are a number of methods for determining the neighbourhood neurons such as rectangular functions, hexagonal functions and Gaussian functions. Step 4: Adaptation The key step in self-organising training is to update the synaptic weights so that the Euclidean distance is minimised as much as possible. The updating strategy is defmed as
w,(/ + i) = w,(0+;7W^,(0k0-w,(/)J
(4)
where Tj{t) is the learning rate at step t, and hj.(t) defines the radius of the topological neighbourhood around the winning neuron i. From the above equation, it can be seen that only the weights of those neurons defined within the topological neighbourhood of the winning neuron / will be updated. The synaptic weights lie outside the neighbourhood will remain unchanged. As the winning neuron / best matches the input vector x in the sense of the selected Euclidean distance metric, the above leaning strategy is able to move the synaptic weight vectors towards the distribution of the input vectors. NUMERICAL EXAMPLES In this section, two simulated examples will be given to demonstrate how the SOM classifies the input vectors, hi both examples, a two-dimensional input vector space is used and a 10 by 10 neuron lattice is adopted. Figure 2 shows the classification results of a two-dimensional randomly distributed input space. Figure 2 (a) plots the original input distributions lie in the rectangular areas of [-0.25, 0.25, -0.25, 0.25] and [0.25, 0.75, 0.25, 0.75] respectively. Clearly they can be classified into two classes by the dashed line. The distribution after 500 epochs is shown in Figure 2(b). It can be seen that most weights are located around the centre of the plane which do not show clear separation. With the increase of iteration number to 1,000 in Figure 2(c), more and more weights are re-organised towards the bottom left and top right comers where the original input distributions are located. When the iteration number is
548
further increased to 10,000 epochs, the distribution of network weights in Figure 2(d) looks more like the original input distribution in that more weights fall into the above rectangular areas. (a) 2-D distribution
(b) 500 epochs
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Figure 2: 2-dimensional input distribution and synaptic weight distributions Figure 3 demonstrates the approximation of two simulated time domain signals. One is a sinusoidal wave with an oscillation frequency of 10 Hz and the other is a randomly distributed noise with amplitudes between [-0.2 0.2]. (b) 500 epochs
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Figure 3(a) shows the input sinusoidal signal with the added noise. As the input dimension is two so the synaptic weights are plotted separately in the following three figures. It is expected that each weight vector should approximate one of the input vectors. Figure 3(b) show the resuh after 500 epochs. It can be seen that the plots of the two weight vectors give some information about the input signals. For instance, the dotted line signal has amplitudes within [-0.05, 0.05] while the dashed line signal lies between -0.75 and 0.50. When the iteration is increased to 1,000 epochs, the amplitudes of both weight vectors increase to some extent. For example the amplitudes of the sinusoidal signal extend between -0.8 and 0.85 and the amplitudes of noise signal are within [-0.08, 0.08]. With the iteration is further increased to 5,000 epochs, both amplitudes are increased correspondingly. Furthermore, the shape of the dotted weight vector looks more like a sinusoidal signal. This implies that SOM networks are trying to approximate the input signals in terms of amplitudes and shapes. ANALYSIS OF REAL DATA To verify the efficiency of SOM networks, experiments were carried out in a reciprocating laboratory and acoustic signals were collected for analysis. Faults were seeded in engine cylinders by adjusting cylinder valve clearances to different levels. The normal clearances for exhaust and inlet valves were set at 0.38/0.20 mm. The exhaust and inlet valve clearances in cylinders 1 and 4 were increased to 0.70/0.40 mm and 0.45/0.50 mm respectively. Eight segments of acoustic signals under the same load and speed were measured and fed to the SOM network with a two dimensional lattice of 15 rows and 15 columns (225 neurons). The training iteration was set at 5,000 epochs in all cases. As the input vector dimension is 8, it is impossible to represent all the synaptic weights in the conventional two or three-dimensional plots. It is noted, from the above examples, the synaptic weights contain the condensed information about the input vectors. Hence it is proposed to compare the averaged spectral information of the synaptic weights. Figure 4 shows the spectral comparison of synaptic weights under 1000 rpm and 30 Nm load.
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The optimised basis functions are illustrated in figure 5 in both the time and frequency domains. The basis functions are very similar to the raw data in the time domain and it may be observed those for the vibration data fluctuate more than the basis functions for the acoustic data (as in the raw data itself). There is also dissimilarity between the individual basis functions as confirmed by the Euclidean distance, probability density functions (PDFs) and dot product [6]. This dissimilarity suggests that the basis functions reflect distinct timedomain features.
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The error signal is defined to be the difference between some desired response and the actual output of the network. eXn) = d,-yXn)
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A < W = -//V5), which were then processed using the FFT. Detection was attempted on the results of the FFT calculation. The clearance ratio (CR) and peak ratio (PR) were chosen to evaluate the performance of adaptive neural filter The clearance ratio (CR) and peak ratio (PR) were defined as follows Clearance Ratio CR-^Y
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where, X are the results of the FFT calculation of input data x, Y are the results of the FFT calculation of output data y, Yj is the peak amplitude in the keyfi-equencyof the signal feature. The relationship between learning rates and evaluation factors (CR factor and PR factor) is shown in Fig 3. The FFT calculation had a data length of 4096 points. Figure 3 shows that there is a good stability range of noise canceling for adaptive neural filter when the learning rates are chosen between 0.0003 and 0.07. When the learning rate is 0.002, the relationship between the width of the sliding window and the evaluation factors (CR and PR) is shown in Fig. 4 The CR and PR increased with the increased sliding window widthfi-om64 to 8192 points. The most suitable result was found at point 8192 for CR and PR, but the rangesfi-om1024 to 8192 were also to be able to satisfy thefilteringrequirements.
574
The variation of adaptive neuralfilteringwithfilteringtime is shown in Fig. 5. The input sequence data was divided into 16 blocks, with 512 samples in each block. Figure 5 shows that the adaptive neural filter will be stable after 0.124 second. The difference between error and actual signal performance of filter for neural network is shown in Fig. 6. Within 0.15 second the network error signal forms a very good estimate of rectangular signal.
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Fig. 6 Difference between Error and Actual Signal Performance of Filter for Neural network
EXPERIMENTAL RESULTS EV AN ACTUAL BEAREVG RUNNING SYSTEM The test bearing was artificially localized defects induced by an electric-discharge machine. The test has three types of vibration under different loads and speeds. Normal bearing vibrations can be caused by in either identical or diflferent bearing types. One defective bearing was installed. A comparison of estimated bearing signal waveform Fig. 8(b) with the raw bearing Fig. 8(a) shows that these two waveforms are very similar. In Fig. 8(c) and (d), the detective bearing waveform is shown along with its spectrum. In Fig. 8, the performance of neural filter is not very clear thought the impact structure (due to the fault) is easily to recognizable, but Kurtosis value is increased to a value of more than raw bearing signal.
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The Kurtosis factors of the three failure sizes for the two filtering algorithms are shown in Fig.9. Generally, the Kurtosis is 3 when the bearing is running under normal conditions. Figure 9 shows that the kurtosis was greater than 3 for three sizes of bearing failure, and that the adaptive neural filtering algorithm is more suitable at identifying bearing faults than the standard LMS algorithm's. The improved ratios of Kurtosis values in three failure sizes (large size, medium size, small size) between the adaptive neural algorithm and the standard LMS algorithm are 3.93, 2.61 and 1.13 times.
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Fig. 8 Performance of Filter for Neural Network and LMS Algorithm
CONCLUSIONS Statistical analysis showed that the ANF method was more able to identify a bearing fault than standard LMS algorithm. The values of Kurtosis in the adaptive neural algorithm are 1.13—^3.93 times as high as they were in standard LMS algorithm. The experimental results also show that there is a wide, stable range (0.0003—0.07) of learning rates for the adaptive neural filter. Reference [1] D. Dyer, R. M. Stewart," Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis", Trans, of the ASME Journal of Mechanical Design, Vol. 100,229-235, April, 1978. [2] S. Braun, B. Datner, "Analysis of Roller / Ball Bearing Vibrations", Trans, of ASME Journal of Mechanical Design, Vol. 101, 118-125, Jan. 1979. [3] G. K Chaturvedi, D. W Thomas, "Bearing Fault Detection Using Adaptive Noise Canceling", Trans, of the ASME Journal of Mechanical Design, Vol. 104, 280-289,1982. [4] Yimin Shao and Kikuo Nezu, D^ection of Self-aligning Roller Bearing Fault Using Asynchronous Adaptive Noise Cancelling Technology, JSME International Journal, Vol.42,No.l, March 1999, pp.33-43. [5] C J. Li, S. M. Wu," On-Line Detection of Localized Defects in Bearings by Pattern Recognition Analysis", ASME of Journal of Engineering for Industry, Vol. Ill, 331-336, November, 1989. [6] "Neural Network Toolbox User's Guide", The Math Works, Inc. [7] Simon Haykin, "Adaptive Filter Theory", Prentice-Hall, Inc. A Simon & Schuster Company Upper Saddle River, New Jersey 07458, 1996.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ANALYSIS OF NOVELTY DETECTION PROPERTIES OF AUTOASSOCIATORS Sang Ok Song, Dongil Shin, and En Sup Yoon Institute of Chemical Processes, Seoul National University, Seoull51-744, Korea ABSTRACT In this work we review PCA and various non-linear PCA methods from the autoassociator point of view. Autoassociator is used to identify and remove correlations among problem variables and can be used to detect abnormality condition of various processes where an early warning of an abnormal condition is required. Feature extraction methods such as PCA and neural network can be an excellent tool of building autoassociator. Several autoassociators based on statistics and neural network have been reviewed and their autoassociative reconstruction properties and abnormality detection performances have been analyzed for a nonlinear 3-dimensional example. Results show that principal curves & neural network, principal curves & splines, and self-supervised MLP successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system. Also these methods can be reliable solutions for novelty detection and their characteristics are discussed. KEYWORDS Autoassociator, novelty (abnormality, fault) detection, PCA, nonlinear PCA, principal curves, SOM INTRODUCTION Autoassociator is a model that the output can estimate the correct value of the input. Autoassociator reduces dimensionality and produces a feature space map resembling the actual distribution of the input. Therefore any feature extraction method can be a tool of building autoassociator. The appHcations of the autoassociator include abnormal condition detection, missing sensor replacement, and so on. In this work we analyze the projection properties and abnormality detection performance of autoassociators. One of the characteristics of abnormality detection problem is that only normal patterns are available while abnormal ones are not. The output of autoassociator reproduce the input and the autoassociation error defined as the Euclidean distance between the input and output vector can be used as a criterion for abnormaUty condition. If the error is small, then the input can be considered as normal and if it is large, then the input can be abnormal. In section 2, we give the brief explanation of the basic structure of autoassociator and review various methods for building the autoassociative model. In Section 3, we analyze the feature extraction properties and abnormahty detection performance of autoassociators for a 3-dimensional mathematical example. 577
AUTOASSOCIATORS We consider the basic structure of autoassociator as follows Auto-assockthn error =
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Figure 1. The basic structure of autoassociator The function G and F are the mapping and dems^ping function of autoassociator and Z is the matrix of feature variables extracted from the input variables. This leads to the dimensionality reduction formulation, where the encoding is given by function G performing mapping from the input space to a lower-dimensional feature space, and the decoding is given by the function F mapping from feature space back to the original input space, x is the reconstructed output with the same dimension as the input space. Therefore, autoassociator can be achieved by estimating function G and F. Principal Component Analysis Principal component analysis (PCA) is a favorite statistical tool for data compression and information extraction. PCA finds combinations of variables that describe major trends in a dataset. Mathematically, PCA relies on an eigenvector decomposition of the covariance or correlation matrix of the process variables. However PCA identifies only linear correlations between variables. Principal Curves and Surfaces The notion of principal curves and surfaces (or manifolds) has been introduced in statistics by Hastie and Stuetzle (Hastie and Stuetzle, 1989) in order to approximate a scatterplot of points from an unknown probability distribution. A smooth nonlinear curve called a principal curve is used to approximate the joint behavior of the two or more variables. The principal curve is a nonlinear generalization of the first principal component and the principal manifold is a generalization of the first two principal components. Conceptually the principal curve is a curve that passes through the middle of the data. For a given distribution, a particular point on the curve is determined by the average of all data points that project onto that point. When dealing with finite data sets, we must project onto a neighborhood of the curve. This self-consistency property formally defines the principal curve. E(X\Z
= argmJn||F(z')- ^' )= F ( G ( X ) )
(1)
where E denotes usual expectation. The individual components of (1) can be conveniently interpreted as the encoding and the decoding mappings. Therefore the procedure of obtaining principal curves is given in two steps. The projection step: This step corresponds to encoder mapping step, where the data are projected onto the current estimate of the principal curve. G(x) = argmm||r(z)-X^f
578
(2)
The conditional average step: This step corresponds to decoder mapping step, where locally weighted regression and smoothing scatterplots are used to estimate F(Z)=E(X|Z)
(3)
After iteration, the Euclidean distance between the data set and estimated principal curve is calculated Self-supervised MLP Autoassociator can also be performed using the multiplayer perceptron architecture to implement the mapping functions F and G in a bottleneck. This approach is called self-supervised operation referring to the fact that during training the output samples are identical to the input samples. Selfsupervised MLP are also known as bottleneck MLP, nonhnear PC A networks (Kramer, 1991), or replicator networks (Hecht-Nielsen, 1995). The simplest form of self-supervised MLP has a single hidden layer of A: nonlinear units and m linear input/output units encoding m-dimensional samples {k < m). In order to effectively construct a nonhnear dimensionality reduction, the mapping functions, F and G in figure 1 must both be nonlinear. This suggests that a 3-hidden-layer network should be used.
Figure 2. The architecture of self-supervised MLP The bottleneck (middle) hidden layer in Figure 2 has linear units. If the training is successful, the final network performs dimensionality reduction the original m-dimensional sample space to the kdimensional space of the bottleneck hidden layer. An MLP network shown in Fig 2 may be conceptually appealing for nonlinear dimensionality reduction and autoassociator. Batch Self-organizing Map Self-organizing map (SOM) is closely related to the principal surfaces approach. The fundamental idea of self-organizing feature map was introduced by Marlsburg (1973) and Grossberg (1976) to explain the formation of neural topological maps, which has been successfully appHed to a number of pattern recognition and engineering appHcation. However, the relationship between SOM and other statistical methods was not clear. Later it was noted that Kohonen's method could be viewed as a computational procedure for finding discrete approximation of principal curves (or surfaces) by means of a topological map of units. The batch version (Luttrell, 1990; Kohonen, 1993) of the selforganizing map algorithm is closely related to the principal curves algorithm. The feature (Z) space can be discretized into a finite set of values called the map. Vectors Z in this feature space are only allowed to take values from this set. An important requirement on this set is that distance between members of the set exists. We will denote the finite set of possible values of the feature space as
'^ = {y/,,y/^,-',y/,}
579
(4)
The elements of this set are unique, so they can be uniquely specified either by their index or by their coordinate in the feature space R*. Since the feature space is discretized, the principal curve or manifold F(Z) is defined only for values Z e T . Therefore this function can be represented as a finite set of centers taking values
Cj=¥{^{j%
J = l,...,b
(5)
In this way the units provide a mapping from the discrete feature space 4^ to the continuous space R'". The elements of ^ define the parametrization of the principal curve or manifold. The encoder funcion G is then, G(x)=^[argmm||c,-X|f]
(6)
Discrete representation of the principal curve, along with a kernel regression estimate for conditional expectation (3) results in the batch SOM algorithm. The locations of the units in the feature space are fixed and take values Ze^. The locations of the units in the input space R'" will be updated iteratively. Principal Curves & Neural Networks The principal curve gives a generalization of the first linear principal component, but the algorithm does not produce a nonlinear principal component model in the sense of a principal loading. Rather, for each data point, an associated score and corrected data point are calculated. One approach to using these scores and corrected data points would be to store them in a computer and interpolate among them when a new data point is measured. Such an approach is cumbersome and depending on the size of stored results, it could be expensive in terms of computer time. As alternate approach, Dong and McAvoy (1996) presented an NLPCA method that integrates the principal curve algorithm and neural networks. The nonlinear function F is defined as the nonlinear principal loading function. For an arbitrary nonlinear relationship expressed by F, a neural network can be an available approach to use to model F because of its universal approximation property (Homik et al., 1989). In this approach, two models are needed. The first one maps the m-dimensional data set onto the ^-dimensional nonlinear principal scores. The second one maps the ^-dimensional principal scores onto an m-dimensional corrected data set. The architecture of the neural network for implementing NLPCA is shown in Figure 3. Pflpdpai Ctirv^
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Figure 3. The architecture of the principal curves & neural networks
580
COMPARISON OF RECONSTRUCTION PROPERTIES DETECTION PERFORMANCES OF AUTOASSOCIATORS
AND
ABNORMALITY
We analyze the abnormality detection properties of following methods for simple 3-dimensional mathematical example. All subsequent analysis was performed using the Matlab 5.2. - Method 1 : PCA - Method 2 : Principal curves & neural networks - Method 3 : Principal curves & splines - Method 4 : Self-supervised MLP This example was used to illustrate nonlinear process monitoring by Dong and MacAvoy (1996). A system with three variables and only one factor is considered. 1 = ^ + ^1
(7)
2=t^-3t + e2 3=_r'+3^'+e3
(8) (9)
where e\, ej, e^ are independent noise A^(0,0.01), ^G[1,2]. hi the first 200 samples, data calculated according to these equations, and these data are taken as the normal condition. 100 samples are used as the training data and other 100 samples are used as the normal test data. After the first 200 samples, there are small changes made in X3, and the system becomes Xi=^ + ei
(10)
x^=t^-?>t-\-e^
(11)
X3 =-1.1/'4-3.2/'+^3
(12)
100 samples are calculated for abnormal test data, hi method 2 and 3, discrete principal curves are estimated by the batch version of self-organizing map algorithm. Principal curve estimate is provided by 10 centers for the training data. In method 2 neural networks are used to obtain principal curves from these centers and reconstructed data fi^om principal curve estimates. In method 2 we use cubic spline interpolation to obtain principal curves from these centers. In method 2 each neural network has 5 hidden nodes and in method 4 the autoassociative neural network has a 3:5:1:5:3 architecture. Next figures show reconstructed data of each method for normal training data. Three methods except PCA can explain nonlinear correlation of the training data. However because the selforganizing map uses a discrete feature space, the output values (Z) of the first network and the input values of the second network in method 2 are only allowed to take values in the principal score value set {0, 1,..., 10}. For this reason the value of reconstructed data in method 2 is one of the values of finite centers. Therefore more centers we use in method 2, the smaller the training error is. Figure 6 shows the reconstructed data for the use of 50 centers in method 2. Method 3 and Method 4 have almost same projection properties. In principal curves & splines the training data points are projected to the closest points on the principal curve. In self-supervised MLP reconstructed data points form a curve that passes through the middle of the data as like the principal curve.
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- Original Function ! • Training Data i O Reconstructed Datai
Figure 4. The projection data point for training data in linear PCA (error=0.8561) - Original Function • Training Data O Reconstructed Data
- Original Function • Training Data O Reconstructed Data
Figure 5. Principal curves & neural networks (10 centers, error=0.2350)
Figure 6. Principal curves & neural networks (50 centers, error=0.0926)
- Original Function • Training Data O Reconstmcted Data
- Original Function • Training Data O Reconstructed Data
Figure 8. Self-supervised MLP (error=0.0727)
Figure 7. Principal curves & splines (error=0.0773)
Next Figures show the SPE charts of four methods for normal (the first 100 samples) and abnormal (the second 100 samples) test data. The dotted line is the threshold that is maximum value of the first 100 samples. The number of values that exceed this threshold for the second 100 SPE can be one of the criterions how well the model detects abnormality.. Figure 9 shows that the SPE of linear PCA is similar for both the normal and abnormal conditions and one of the SPE of abnormal data exceed the threshold. It is not surprising that linear PCA fails to detect this abnormality because the
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example data has nonlinear correlation and linear PCA cannot capture the nonlinear relationship. For method 2 (principal curves & neural networks) with 10 centers, 40 of the second 100 SPE values exceed the threshold, while for using 50 centers, 47 of the second SPE values exceed the threshold. It can be seenfromthis result that Method 2 with 50 centers can detect the novelty more effectively than 10 centers. Because the value of reconstructed data is almost same as the value of the closest center, if the distances between centers too large to discriminate between noise in the normal data and abnormality, the SPE for this model cannot the criterion of abnormality detection. However if too many centers are used to obtain a good result, you can have an over-parameterized model. There exist some kind of tradeoff problem between the number of center used and the model complexity. For method 3 (principal curves & splines), 53 of the second SPE values exceed the threshold and for method 4 (self-supervised MLP) 51 exceed the threshold. In principal curves & splines algorithm principal curve can be obtained from small number of centers by using cubic spline interpolation. Input data points are projected to the closest points on the principal curve, the SPE of abnormal data become large. Therefore abnormality conditions can be detected well using small centers in comparison with method 2. However principal curves & splines algorithm with many centers produces an over-parameterized model as like principal curves & neural network algorithm. Self-supervised MLP has as similar performance as principal curves & splines. It is not very difficult to train network for this 3-dimensional example. However sometimes the network is not trained well and then the abnormality performance is very poor. It is expected that if the system is more complex, it is more difficult to train MLP network. • - ^ ,........>....,, a o CI u
9 -^
Define the problem
Measure what you care a b o u ^
Statistically find root causes ^ x
Mobilise change initiative^x^
^ Define
Measure
Analyse
Improve
Control
Methodology Figure 1. DMAIC Methodology vs. ROI
TOOLS AND TECHNIQUES EMPLOYED TO IMPLEMENT SIX SIGMA STRATEGY Table 2 shows some of the tools and techniques used to successfully implement Six Sigma Strategy. TABLE 2 Statistical Process Control Charts: Multiple Charts, X-bar, R Charts, Pareto Charts, Process Capability & Performance Indices, Moving Average/Range Charts, EWMA Charts, Short Run charts, CuSum Charts, Runs Tests, Multiple Process Stream, etc. Process Analysis: Process/ Capability Charts, Ishikawa (Cause & Effect) Diagrams, Gage Repeatability & Reproducibility, Variance Components for Random Effects, Weibull Analyses, Sampling Plans, etc. Design of Experiments: Fractional Factorial Design, Mixture Design, Latin Squares, Residual Analysis & Transformation, Taguchi Design, Central Composite Design, etc. Other Tools and Techniques: Enterprise Resource Planning (ERP), Production Planning System (PPS), Computer Aided Design (CAD), Manufacturing Execution System (MES), Suppliers Assessment Management (SAM), Audit Management (AM), Design and Process FMEA, Risk Assessment, IT Integration, Quality Management Software such as CAQ = QSYS, BabtecCAQ, eVerest, Active Flow, Solution Prosper, Paradigm II, Dataputer, STATISTICA software, Galaxy Metrology software, Quick Serve, CHARTrunner 20QQ, SQCpack for windows, PQRTspy Plus, etc.
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SOME REPORTED ECONOMIC BENEFITS OF IMPLEMENTING SIX SIGMA STRATEGY Six Sigma has achieved outstanding results for companies world-wide. Pioneered by Motorola in the mid 1980s it is no surprise that the global industries have quickly responded to this revolutionary change in management culture. Here are some reported benefits of this highly innovative quality management discipline. "In 1997, General Electric announced that it would save $500 million that year because of Six Sigma and by 1998 the programmes had risen to $1.2billion" - Quality World (1999). 'By completing 5 to 7 projects per year per Black Beh the company will add in excess of US$1 million per Black Belt to it's bottom line' - T. Pyzdek author of The Complete Guide to Six Sigma. " Commonwealth Health Corporation, a 478 bed medical center in Kentucky, began its journey to implement a Six Sigma improvement culture over three years ago. Results have been overwhelming as the medical center reports a reinvigorated and transformed management culture. Within a mere 18 months, errors in one ordering process were reduced over 90%, overall operating expenses had been reduced by $800,000, and employee survey results had improved by 20%. These results were from a single division within the organisation. Now the medical center has realized improvements in excess of $1.5 million and is expanding the program to other areas." - G.T, Lucier and S. Sheshadri, Strategic Finance (May 2001). 'Lockheed Martin saved $64 million on the first 40 projects. Motorola claim dramatic results: Productivity up an average of 12.3% per year; Reduced cost of poor quality by more than 84%; Saved more than $11 billion in manufacturing costs. GE 1995 - 1998 Company side savings of over $1 billion; Estimated annual savings to be $6.6 billion by 2000' - Graeme Knowles, Warwick Manufacturing Group (2000). AUiedSignal Inc implemented Six Sigma Breakthrough Strategy with the goal of increasing productivity of 6% each year in its industrial sectors. These initiatives allowed operating margin in the first quarter of 1999 to grow to a record of 14.1% from 12% one year earlier. Since the CEO implemented the programme in 1994, the cumulative impact of Six Sigma has been a savings in excess of $2 billion in direct costs. - M. Harry & R. Schroeder, Six Sigma (2000). ' ... in fact, a number of prominent companies in industries from financial services to transportation to high-tech are quietly embarking on Six Sigma efforts. They're joining others, ..., including Asea Brown Boveri, Black & Decker, Bombardier, Dupont, Dow Chemical, Federal Express, Johnson & Johnson, Kodak (which had taken $85 million in savings as of early 2000), Navistar, Polaroid, Seagate Technologies' P.S. Pande, R.P. Neuman, R.R. Cavanagh, The Six Sigma Way (2000). Six Sigma is having a significant impact on UK's business. It is now regarded as the intellectual capital of the UK's modem economy and a wealth creation dynamic force. Honeywell Control Systems, Tate and Lyle, Volvo Cars Market Area Europe, David Hutchins International, Glaxo Wellcome, Catalyst Consulting Ltd, Eastman Kodak Company, General Domestic Appliances, Sun Microsystems, Marconi Services ICI EUTECH, DuPontSA have recently reported similar success stories. - Conference
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Proceedings on Practicalities of Introducing Six Sigma into Manufacturing & Process Industries held in London in January 2001.
SIX SIGMA INITIATIVES IN THE FIELD OF COMADEM Industries worldwide employ various assets in their manufacturing/processing activities. Keeping these assets in a fit for use conditions all the time and to make them available when and where they are needed is essential. In real life, systems do fail for one reason or the other. There are ample evidences to show the various modes of failure in engineering/manufacturing/process systems. See Figure 2. Causes for Production (Uptime) Losses
• Chronic Failures: Faster loosening, key wallow, misalignment, oil/air leaks etc. • Sporadic Failures 70%
Figure 2 Drastically reducing the risks of failures/defects in industrial systems not only prolongs its life expectancy, it increases the uptime, enhances the quantity, quality, reliability and availability of the output and significantly improves the life cycle cost and profit margin of the company. Six Sigma Strategy has clearly paved the way to realise zero defect engineering goal in today's consumer oriented, high speed and complex global economy. It is now regarded by many as an effective proactive integrated management strategy and is considered as the main central theme in all major decision making processes. Its sphere of application encompasses pharmaceutical, chemical, electrical, electronic, medical, transportation, service, defence, education/training and many other sectors/industries worldwide. A Design for Quality methodology (Design for Six Sigma - DFSS) based on the utilisation of statistical methods and tools is embedded in a New Product Introduction (NPI) process to develop new gas turbine engines able to meet customer performance expectations ( Bongini, Citti, Mezzedimi and Tosnarelli (2000)). Many industries and engineering disciplines use the Design and Process Failure Modes and Effects Analysis (FMEA) for different purposes and in a variety of ways. For the design engineer, the FMEA is used to anticipate the ways in which a design will fail. In this way, the design can be improved or the effects of the failure avoided. For the Reliability or Maintenance Engineer/Manager, the FMEA is used to allocate
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timely resources to resolve various issues. While in one case (design, reliability/maintenance) the focus of the FMEA is on the failure modes of the components/systems, for a RCM analysis, the focus is on the functions of the system and the w^ays by which the functions can fail. The current thinking in the petroleum industry is to integrate RCM and Six Sigma processes to maximum benefits (Lee Pendleton (2001)). Six Sigma Strategy can be gainfully implemented as an on-going, continuous improvement, proactive maintenance management, and quantitative/predictive methodology in several Best Practices programmes by both large and SMEs. No doubt, with increasing public awareness and understanding of this innovative and revolutionary dynamic process, the nature of manufacturing will change in many drastic ways.
THE ROLE OF SIX SIGMA IN A KNOWLEDGE - BASED ECONOMY There is an urgent need to discover, generate and disseminate this newly acquired knowledge for the benefit of community. A number of organisations are offering education and training programmes in this field. Mike Harry and Richard Schroder have established Six Sigma Academy, Inc. and they can be contacted on www.6sigma.com. We need a constant stream of re-skilled flexible work force to actively contribute to the new wealth creating economy. Here is a unique opportunity for universities, professional institutions and industries to join forces to create innovative, nationally accredited and industrially relevant undergraduate/postgraduate/NVQ programmes. The Governments and the European Community should take proper initiatives to launch suitable knowledge imparting schemes to increase the awareness, public understanding and Partnerships/collaborative/foresight programmes in this world-class campaign. The time is now right to formulate international standards in the training/ accreditation/certification of Six Sigma specialists. Individuals should also take full responsibility to further their own career developments.
CONCLUSIONS Condition Monitoring and Diagnostic Engineering Management is a proactive integrated management interdiscipline. There are well-established/ tried and tested tools, techniques, methodologies and strategies in this field that can be judiciously selected and profitably exploited to maximum benefits. Six Sigma should be of paramount importance to every forward-thinking executive, manager, engineer, technologist, policy maker and service provider determined to make their organisation a world class.
REFERENCES 1.
2.
N.M. Tichy & S. Sherman (1993). Control Your Destiny or Someone Else Will: Lessons in Mastering Change - from the principles Jack Welch Is Using to Revolutionize GE. Harper Business. New York. M.J. Harry & J.R. Lawson (1994). Six Sigma Producibility Analysis and Process Characterization. Addison-Wesley.
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3.
W.J. Kolarik (1995). Creating Quality: Concepts, Systems, Strategies and Tools. McGraw-Hill. 4. H. Mikel (1999). Six Sigma: The Management Breakthrough Strategy Revolutionizing the World's Top Corporations. Bantom Doubleday Dell. 5. P.S. Pande, R.P. Neuman & R.R. Cavanagh (2000). The Six Sigma Way. McGraw-Hill. 6. M. Harry & R. Schroeder (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Currency. 7. wsvw.sixsigmaexchange.com. The independent on-line information gateway for Six Sigma professionals world-wide. 8. D. Bongini, P. Citti, V. Mezzedimi & L. Tognarelli (2000). New Product Introduction and Design for Six Sigma Processes Integration in Gas Turbine Design. Proceedings of the 3^^ International Conference on Quality, Reliability and Maintenance. Professional Engineering Publishing Ltd. London. 9. K. Young (2000). Process Control, Variability Reduction & Six Sigma Performance. Proceedings of a one-day seminar held at the University of Warwick in November. 10. G.T. Lucier & S. Sheshadri (2001). GE Takes Six Sigma Beyond the Bottom Line. Strategic Finance. May Issue. 11. L. Pendleton (2001). The Application of RCM2 to Equipment used to Manufacture Water Chillers. Proceedings of the Maintenance and Reliability Conference MARCON 2001. Organised by the University of Tennessee in May 2001.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Ail rights reserved.
MONITORING EXHAUST VALVE LEAKS AND MISFIRE IN MARINE DIESEL ENGINES A. Friis-Hansen' and T. L. Fog^ ' Department of Mechanical Engineering, Building 101E Technical University of Denmark, DK-2800 Lyngby ^ Research & Development, MAN B & W Diesel A/S, Teglholmsgade 41, DK-2450 Copenhagen SV
ABSTRACT The objective of the present study is to identify efficient classifiers for detection of two different failure modes in marine diesel engines, namely exhaust valve leaks and defective injection (misfire). The classification is performed on the basis of structure-borne stress waves from recorded RMS Acoustic Emission (AE) data. These were obtained by experiments with a two-stroke four cylinder 500 mm bore low-speed marine diesel engine with an approximate output of 10.000 BHP. The study may be seen as a step towards a diagnostic method, which can assist in the interpretation of measurements and detection of causes of abnormalities during management of the engine. The dataset is analysed for identification of the failure modes: Normal exhaust valve (i.e. no leak), small leak and large leak. Measurements from the compression stroke are found to hold more information about exhaust valve leaks than other subsets analysed. By using a simple equation relating the cylinder pressure to the crankshaft angle as well as a data-fitting scheme, it is possible to arrive at a very efficient classifier (no misclassification). This classifier is flexible because it is based on a physical principle and should therefore be relatively easy to adapt to other engines and sampling frequencies. Defective injection (or misfire) may be detected by exploiting symmetry properties of the above mentioned equation and compare it to the measured data. The results are in general very encouraging and the solution may be seen as a hybrid approach combining domain knowledge and data processing.
KEYWORDS Condition monitoring, Exhaust valve leaks, Acoustic emission, Diesel engines, Classification
INTRODUCTION The purpose of the exhaust valve is to seal the combustion chamber from the surroundings during compression, thus securing maximum pressure in the cylinder during the combustion event. This again is necessary for achieving maximum engine performance in terms of output power. Exhaust valve leaks (or bum-through) are usually caused by dent marks on the sealing face of the valve. Due to hot corrosion, the cross-sectional area of the leak will increase rapidly and the engine output performance will decrease 641
correspondingly. If the leak is detected early, reconditioning of the damaged valve may be possible. But more often the valve has to be replaced. In both cases the repair and replacement is a time consuming process that may cause costly transport delays. It is therefore of great importance to be able to monitor the degradation of the exhaust valve so that the associated maintenance work can be properly scheduled and thereby secure continuous operation of the vessel. Experiments were carried out on the Research Engine of MAN B&W Diesel A/S (see Fog (1998)). Analysis of the obtained dataset has been reported in Fog (1998) and Fog et al. (1999) in which the classification task was performed using an ensemble of neural networks which outputted posterior probabilities of the valve condition. Other studies of detection systems for exhaust valve leaks have been reported, for instance in Bardou & Sidahmed (1994).
EXPERIMENTS AND DATASET The Research Engine is a two-stroke, four-cylinder low-speed diesel engine with a 500 mm bore. From the series of experiments reported in Fog (1998), it was concluded that acoustic emission (AE) signals were better indicators of mechanical events than the other investigated detection methods (temperature of exhaust gas, cylinder pressure and vibration measurements by accelerometers). AE sensors measure structure-borne stress waves released as energy when deformation of the material occurs. AE has proved superior to the other measurements obtained, indicating sensitivity to both mechanical and fluidmechanical events, where acceleration for instance is sensitive only to the mechanical activity. In this study, the only signal type investigated is the AE signals. Two identical AE sensors were mounted in two different positions on the exhaust valve housing as shown in Figure 1.
Temperature
HB^K^
AE Sensor I I ^ ^ ^ ^ ^ ^ H p
Accelerometer
A£ Sensor U
Figure 1: Approximate positions of the two AE sensors on the outside surface of the valve housing. The accelerometer and the thermocouple are not used in this study. See also Fog (1999). During operation of the engine, AE signals were recorded from each of the sensors. In addition, a shafttiming signal with a resolution of 2048 angle-specific pulses per revolution and a Top Dead Centre (TDC) signal were recorded. As a pre-processing step the AE signals were synchronised with the TDC of the piston so that each data series comprises exactly one revolution starting at the TDC. Furthermore, the signal is trigger-resampled into 2048 points per revolution, so that one data series comprises 2048 angleequidistant values. The experiment was performed for four different engine load cases: 25%, 50%, 75% and 100%. For each engine load case, three failure modes were investigated: No exhaust valve leak (normal operation), small leak (approx. 4 mm^ cross-section), and large leak (approx. 20 mm^ crosssection). When the engine is used for propulsion of a ship with a fixed pitch propeller, the load is regulated by the engine speed. In this situation, the engine is said to work on the propeller curve. In this study only data obtained from the propeller curve is analysed. A series of experiments was performed without fuel injection in order to simulate a defective injector, also called misfire. The experiments 642
comprised a total of 367 datasets (engine revolutions), of which 21 cases have misfire. As an example, a dataset for a large leak at 50% load (no misfire) is shown in Figure 2.
2500
1000
Figure 2: Measurements for an entire revolution, 50% load, propeller curve. Large leak. PRELIMINARY DATA ANALYSIS In Figure 2 it is illustrated that a relatively low vibration level is observed in the beginning of the series (part I - data point 1 to approximately 700). In the middle (part n - data point 700 to approximately 1400), the data shows a very irregular pattern and towards the end (part HI), a more regular pattern is resumed. The datasets may thus be partitioned into three qualitatively distinct parts. The physical explanation is that the valve is closed for the first part (combustion), open for the intermediate part (exhaust and scavenge) and closed in the last part (compression). It seems reasonable to detect exhaust valve leaks from part I and part in where the exhaust valve is closed. Hence, the portion of the dataset where the valve is open is neglected. If the valve is closed and has a leak, the pressure difference between the inside and the outside will cause gas to escape through the leak and emit a "hissing noise". The pressure reaches its maximum during combustion, but in order to avoid disturbance from injection noise and noise from the combustion process itself, the datasets from the compression stroke are expected to contain more consistent information about leaks than the other parts of the series do. In Fog (1998) the injection timing as well as the timing of the opening and closure of the exhaust valve is given for different operating conditions. The latest closure angle is 211.1° and the earhest injection angle is 352°. To eliminate clutter from closure of the valve and avoid injection noise, the analysis is restricted to comprise the portion of the data from the crankshaft angle 300° - 350°, corresponding to data points 1706-1992. Examples of such measurements are given in Figure 3. It is seen that the noise level increases significantly with the leak size.
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1750
1800
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1900
1700
1950
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2000
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1850
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Figure 3: Measurements and mean level for compression stroke, 50% load, propeller curve. Upper left panel: normal condition, upper right panel: small leak, lower panel: large leak.
As a first attempt to gain knowledge of the dataset, the second order statistics are computed. The mean noise level \i of the dataset distinguishes perfectly between no leak and small leak. The distinction between small and large leak is associated with a misclassification rate of 4.4%. However, when conditioning on the load case the classification is 100%.
CLASSIFIER BASED ON DATA FITTING OF A SIMPLE PHYSICAL MODEL The basic assumption of this section is that if gas is escaping through a leak, then the general noise level is proportional to the pressure difference between the inside and the outside of the compression chamber. We further assume that the AE signal s(v) measures the general level of the hissing noise as a function of the crankshaft angle v. The following model then applies: s{v)=k,{P,„-P^J
(1)
where k] is a constant. Pin is the pressure in the combustion chamber and Pout is the pressure on the outside of the cylinder. Pout is assumed to be much lower than P,„ and is therefore neglected. Based on the indications of the preliminary data analysis it is assumed that the noise level is dependent on the leak size so that the level of degradation can be estimated. This dependence relation is unknown and therefore not accounted for in the model (Eqn. 1) but the assumption is confirmed later. A general expression for the 644
pressure in the cylinder as a function of the crankshaft angle a is searched for. In Figure 4 a schematic drawing of the cylinder, piston, piston rod and crankshaft is shown.
Figure 4: Geometry of cylinder, piston position and crankshaft angle. Geometry considerations yield x - r - a zo'^a - h cosy^ - k. By considering the top dead centre (TDC) position of the piston, we also see that r^m+k+b+a and by insertion X=m + a(l - cos «) + Z?(l - cos )^)
(2)
Assuming an adiabatic process for an ideal gas in a confined space at constant temperature, PiV\=P2V2, where P is the pressure and V is the volume. The volume of the cylindrical combustion chamber is given by V=xA where A is the cross-sectional area of the cyUnder. During the compression stroke, all valves are closed so the mass of the gas in the combustion chamber is constant, and the pressure P2 may be computed from (3) ^
V2
^
A(m + a(l - cos Cir) + ^ ( l - c o s )^))
The angle )5 is ranging from zero to /3max given by sin j3max= a/b- If we assume b»a, the expression for P2 may be written
c,
then cos J3 = 1 and
(4)
C2 -COS a
in which Ci=PiVi/(Aa) and C2-l+m/a. Although the model is based on many idealisations, a simple model for the pressure as a function of the crankshaft angle a has now been established. For the noise level to be proportional to the pressure, a number of further assumptions are needed. First, that the leaks are too small to decrease the pressure considerably in the combustion chamber compared to normal operation. Secondly, that the transmission of the stress waves through the structure as well as the amplification of the sensor signal are linear. The exact nature of transmission and amplification is unknown, but Eqn. 1 is general enough to take into account a proportionality factor which in a primitive way accounts for the model uncertainty. Hence, the general model can be formulated s{a) =
0, 62-cos a
(5)
where Oj = k2Ci and ft :•C2' As an empirical justification of the model, good agreement with the dataset is shown in Figure 5.
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Figure 5: Curve fitted to data by a non-linear optimisation algorithm. Large leak, ft = 506 and 62 -L06. The model contains only two parameters to be estimated, but unfortunately, linear models are not applicable because the problem cannot be formulated as the general linear model: Y=xfe4-£,
(6)
where 0 - (61 , 61 f is the column vector of parameters to be estimated, x is a known matrix solely dependent on the independent variable (here the sampled time steps t e {1706, ... , 1992} and Y is a column vector of the measured data points. Et is the vector of error terms. Instead, a non-linear optimisation algorithm (Levenberg-Marquardt, see e.g Ljung (1987)) is used. This is more timeconsuming and convergence to the global minimum cannot be guaranteed. However, the global minimum of the objective function is relatively well defined in the case where a leak is present, because the minimum value of the fitted function is given by s'(amin)= Oi /(02'Cos(300°)) and the maximum is governed by s'(amax)= 6i/{62-cos(350°)). Both of these have limited possible range, and the minimum of the objective function should therefore be well defined and facilitate convergence. In cases where no leaks are present, the general noise level does not change much during compression, see Figure 3 (top left). The signal is thus stationary around its mean value, and the algorithm searches for values for which the cosine term of Eqn. 5 becomes negligible and the ratio O1/O2 expresses the mean noise level. The algorithm does not stop until the tolerance limit has been reached. RESULTS A number of different classifiers based on the optimisation result Oj and ft are tested. It is found that the following quantities are poor classifiers: the residual norm, ft , s'iOmin), s'{amax) and ft/ ft. Using 7/ft aS a classifier it is possible to discriminate completely between the leak sizes but an even more effective classifier can be formed by the ratio: ^i=-
s\(X^)
^2~cos(300')
^'(O^min)
{d) for specific pulse waveform. TABLE 2: KULLBACK-LEIBLER INFORMATION KL AND KULLBACK-LEIBLER DIVERGENCE ID FOR THE SPECIFIC PULSE WAVEFORMS
1
KL 0.8632 3.2739 L0249 L0747
regular Dulse train pulse train phase shifting single pulse missing single pulse phase shifting |
ID 0.7499 6.5754 2.0036 2.1409
CONCLUSION We proposed the new method useful for the condition monitoring and diagnosis of reciprocating machine or intermittent operating machine, in which we introduced the two new function, namely (1) angle density function P[6) and (2) information function lD[d) of the condition variable such as vibration signal, sound, or pressure signal. Using simulated pulse train signal, we confirmed the effectiveness of this method clearly in Figure 3 and Table 2. Reference Bloch H. P. (1995), yl Practical Guide to Compressor Technology, McGraw-Hill. KuUback S. (1959), Information Theory and Statistics, John Willy & Sons, Inc. 662
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
EXPERIMENTAL RESULTS IN SIMULTANEOUS IDENTIFICATION OF MULTIPLE FAULTS IN ROTOR SYSTEMS N. Bachschmid, P. Pennacchi, A. Vania Dipartimento di Meccanica, Politecnico di Milano Via La Masa 34,1-20158 Milano, Italy
ABSTRACT The topic of the fault identification in rotor systems has been deeply analysed in literature both by theoretical studies and field results. However usually they consider one fault only, while the presence of multiple simultaneous faults is not taken into consideration. The method proposed in this paper is a model based identification method that requires the definition of the model of the system (rotor, bearings and supporting structure) and of the faults. It handles simultaneous multiple faults by means of a least square identification in frequency domain. The method is here vahdated experimentally using the test-rig (two coupled rotors, four oil film bearings and flexible foundation) buih by Politecnico di Milano. By this way some known faults can be appHed and the results of the simultaneous identification can be verified. KEYWORDS Rotor dynamics. Identification, Diagnostics, Multiple faults, Model validation. Experimental results. INTRODUCTION During the monitoring of a rotating machine, generally it is suggested to activate a diagnostic process when the vibration vector goes off the acceptability region; the vector differences with respect to the reference situation are then used for the identification procedure. These vibrations are generally due to a single fault, since the effect of the residual original unbalances and bows has been subtracted. Many recent contributions are available in literature about single fault identification: Kreuzinger-Janik & Irretier (2000) use a modal expansion of the frequency response function of the system, on both numerical model and experimental results, to identify the unbalance distribution on a rotor. Markert et al. (2000) and Platz et al. (2000) present a model in which equivalent loads due to the faults (rubbing and unbalances) are virtual forces and moments acting on the linear undamaged system model to generate a dynamic behaviour identical to the measured one of the damaged system. The identification is then performed by least square fitting in the time domain. Edwards et al. (2000) employ a model based identification in the frequency domain to identify an unbalance on a test-rig. A more comprehensive approach, able to identify several different types of faults and to discriminate among
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faults which generate similar harmonic components, has been introduced by Bachschmid & Pennacchi (2000). This method has been experimentally validated on different test-rigs and real machines, as reported in Bachschmid et al. (2000), for many types of faults, such as unbalances, rotor permanent bows, rotor rubs, coupling misalignments, cracks, journal ovalization and rotor stiffness asymmetries. However in some cases a second fault can be generated as an effect of the first fault: a thermal extended bow, or an increasing unbalance due to erosion or deposit of particles, can cause a rub in a seal and the consequence is the an arising local bow. Sometimes the reference situation is not available, so the multiple fault identification method could enable to identify both the original unbalance or bow and the arising fault. The model based identification method proposed in this paper is a generalization of the method proposed by Bachschmid & Pennacchi (2000) and requires the definition of the model of the system (rotor, bearings and supporting structure) and of the faults. It handles simultaneous multiple faults by means of a least square identification in frequency domain. IDENTinCATION OF MULTIPLE FAULTS IN FREQUENCY DOMAIN Denoting by x the change in the vibration vector due to the fault(s), the general equation that models the behaviour of system, can be written as: M x + Dx + K x = F^(0
(1)
where F//) is a system of equivalent external forces, which force the fault-free system, represented by M (mass matrix), D (damping matrix) and K (stiffness matrix). Otherwise, if a reference case is not available, the vibration vector x represents the total vibration due to both the original unbalance or bow and the arising fault(s). This way, the problem of fault(s) identification is then reduced to a force identification procedure with known system parameters. Keeping in mind that the final goal is the identification of faults, this approach is preferred since only few elements of the unknown fault forcing vector are in reality different from zero, which reduces significantly the number of unknowns to be identified. In fact, the forces that model each fault are considered to be applied in not more than two different nodes along the rotor. Nowadays, experimental vibration data of real machines are often collected by CM systems and are available for many rotating speeds, typically those of the run-down transient that, in large turbogenerators of power plants, occurs with slowly changing speed, due to the high inertia of the system, so that the transient can be considered as a series of different steady state conditions. This allows to use these data in the frequency domain. Assuming linearity of the system and applying the harmonic balance criteria from Eqn. (1), we get, for each harmonic component: [-(/2Q)'M + //2QD + K]X„=F^^(Q)
(2)
where the force vector F^ , has to be identified. This force vector could be function of the rotating speed Q or not depending on the type of the fault. It is worth to stress that if the presence of several faults (fi. m faults) is considered, then the force vector F^^ is composed by several vectors Fj.|\ l?(2)
T^(m) .
Few spectral components in the fi-equency domain (generally not more than 3, in absence of rolling bearings and gears) X„, measured in correspondence of the bearings, represent completely the periodical vibration time history.
664
Moreover, the ^* fault acts on few d.o.f. of the system, so that the vector F]-*^ is not a full-element vector which is convenient to be represented by: F}*^(Q) = [Ff^]A^*^(Q)
(4)
where [F|*^] is the localisation vector which has all null-elements except for the d.o.f. to which the forcing system is applied and A^*^(0) is the complex vector of the identified defects. The vector [F{*^] does not give just the assumed position of the defect but also expresses the link between the force fault system and the modulus and phase of the identified fault that produce it. Eqn. (2) can be rewritten, for each harmonic component, in the following way: [ E ( « Q ) ] X„=J;F]/^(Q) = F^^^(Q)
(5)
where [ E ( « Q ) ] is the system dynamical stiffiiess matrix for the speed Q and for the n^^ harmonic component. The identification method can be applied for a set of p rotating speeds that can be organized as a vector: (6) Then matrix and vectors of Eqn. (6) have to be expanded:
IF(Q,) E{nQ,)
0
0
0
0
E{na^)
0
0
'
1=1 m
[E(WQ)] X„ =
£F(n,) /=l
0
0
= F/.(Q)
(7)
0 E(«QJ
Under a formal point of view, it is unimportant to consider one or p rotating speeds in the identification. The fault vector is the sum of all the faults that affect the rotor as stated in Eqn. (3). Matrix [ E ( « Q ) ] can be inverted and Eqn. (5) becomes X, = [ E ( « Q ) ] - ^ F ^ (Q)=a„(Q).F^^(Q)
(8)
where a„(Q) is the inverse of [ E ( « Q ) ] . Reordering in a suitable way the lines in Eqn. (8), by partitioning the inverse of the system dynamical stif&ess matrix, and omitting from «„ and F^ the possible dependence on Q for conciseness, we obtain:
(9)
665
where X^
is the complex amplitude vector representing the measured absolute vibrations in
correspondence of the measuring sections and X^ is the vector of the remaining d.o.f of the rotor system model. Using the first set of Eqns. (9), the differences 6„, between calculated vibrations X^ and measured vibrations X^„, can be defined, for each harmonic component, as: d„=X,,-X„„,_=a,,-F,,-X,„^
(10)
The number of equations «£ (number of measured d.o.f) is lower than the number ny (number of d.o.f of the complete system model) which is also the number of elements of F^ . But, as said before, F,^ becomes a vector with many null-elements, even if the fault is not one only, so that the number of unknown elements of F^ is smaller than the number of equations. The system therefore has not a single solution for all the equations and we have to use the least square approach in order to find the solution (identified fault vector) that minimises the differences which are calculated for all the different rotating speeds which are taken into consideration. A scalar relative residual may be defined by the root of the ratio of the squared 8„, divided by the sum of the squared measured vibration amplitudes Xg„, :
S. =
[a./F,^-X,,J »
Y
[a,^-F,,-X,,J T
(11)
Y
V
By means of the hypothesis of localisation of the fault, the residual is calculated for each possible node of application of each defect. This fact implies that, if we indicate with Zk the abscissa along the rotor in correspondence to the J^^ fault among m fauhs, the relative residual in Eqn. (11) is a surface in a V"^' space, in other terms: : / ( z , , Z2,...,z,,..., z^)
(12)
Where the residual reaches its minimum, i.e. the minimum of the surface in Eqn. (12), there is the most probable position of the fault. Figure 1 shows an example of the residual surface. The corresponding values of F^ give the modulus and the phase of the idenfified faults. The relative residual gives also an estimate of the quality of the identificafion, since it results the closer to zero the better the identified fault corresponds to the actual one; this follows easily from the analysis of Eqn. (11). EXPERIMENTAL VALIDATION The proposed method has been also tested by means of experimental results obtained on the MODIAROT (Brite Euram Contract BRPR-CT95-0022 MOdel based DIAgnostics in ROTating machines) test-rig designed by the Politecnico di Milano for analysing the effects of different malfunctions on the dynamic behaviour of rotors. The test-rig, shown in Figure 2, is composed of two rigidly coupled rotors driven by a variable speed electric motor and supported on four elliptical shaped oil film bearings. The rotors have three critical speeds within the operating speed range of 0-6000 rpm. The rotor system is mounted on a flexible steel foundation, with several natural frequencies in the operating speed range. In this case the foundation has been modelled by means of a modal representation. Two proximity probes in each bearing measure the relative shaft displacements, or the journal orbits; two accelerometers on each bearing housing measure its vibrations, and two force sensors on each bearing housing measure the forces which are transmitted to the foundation. 666
Relative residual for the Identification of 2 faults (1 x rev. component): 1 Unbalance & 1 Local Bow
100
120 100 Rotor nodes
Rotor nodes
Residifaf surface mmimum Residual (overall): 0.000 Node: 78 (1st fault Unbalance) 93 ^st fault Local Bow)
0 0 Module: 4.006-^000 [kgni] (1st fault Unbalance) Phase: 20.0 H (1st f^ult Unbalance) 7.00e+006 [Nm] (2nd fault Local Bov/) 50.0 H (2nd fault Local Bow)
Figure 1: Residual surface in case of simultaneous identification of two faults. The location of the faults is in the minimum of the surface.
Figure 2: MODIAROT test rig of Folitecnico di Milano.
667
The absolute vibration of the shaft is calculated by adding the relative displacement measured by the proximitors to the absolute bearing housing displacement, which is obtained integrating twice the acceleration measured by the accelerometers. The force measurements were not used in this case. A first run-down test was performed in order to obtain a reference vibration data due to the weight and the unknown unbalance force and unbalance moment. Then, in order to simulate two faults, two masses were added to the rotor on the 2"^ and 7^^ balancing plane (see Figure 2) and a second run-down was performed and the total vibration obtained. Then, the first measurements were subtracted from the last ones in order to obtain the vibration vector x due to the faults. These difference vibration vectors for bearings U2 and #3 are reported in Figure 3 and Figure 4 . The frequency response diagrams need some comments: i) the first natural frequency of the long shaft are about 950 rpm in vertical and 1150rpm in horizontal direction; ii) the very high peak in vertical direction corresponds to the third mode of the foundation (2256 rpm), while the others at 1350 and 2000 rpm are horizontal modes of the foundation; iii) even if the test-rig is built to operate in the range 0-6000 rpm, the rotating speed at which the measurements are taken is limited to the range 550-2700 rpm, the upper limit is due to the fact that system non-linearities become more significant over 2700 rpm and the model fitting is not so good as for lower rotating speeds. .|g«
Measuring Plane 2 - Vertical & Horizontal Directions - 1x Rev. Component
800
1000
1200
1400 1600 1800 Rotating Speed [rpm]
2000
2200
2400
600
2600
Measuring Plane 3 - Vertical & Horizontal Directions - 1x Rev. Component
600
1000
1200
1400 1600 1800 Rotating Speed [rpm]
2000
2200
2400
2600
2400
2600
Nvv—^Hf^'^^'VV^^
600
800
1000
1200
1400 1600 1600 Rotating Speed [rpm]
2000
2200
2400
600
2600
800
1000
J Villi
rsii~\
1200
2000
1400 1600 1800 Rotating Speed [rpm]
2200
Figure 3: Experimental vibration differences in Figure 4: Experimental vibration differences in bearing #2. bearing #3. Since experimental data are used, some caution should be taken into account performing the identification procedure. Generally the use of data close to the resonance peaks leads to a poor identification, also in the case of a fault only. Anyway a first attempt has been done without taking into account this fact by choosing the measures corresponding to a set of 13 equally distributed rotating speeds within the available range. The results of the identification are reported in TABLE 1. TABLE 1 Identification results in the range 550-2700 rpm 550-2700 rpm Type Node Module Phase Residual Node Module Unbalance 10 3.6e-4 kgm -90° 4.2e-4 kgm 8 0.579 Unbalance 35 3.6e-4 kgm -90° 3.81e-4kgm 36
668
Phase -82.8° -89.7°
Error on module 17% 6%
Error on phase 4%
0.2%^ J
Even if the relative residual of the identification in rather high, the location of the identified fauhs can be considered fairly good since they are on the same flywheel masses of the actual faults. The error on the phase is reduced, but this fact is quite common in least square identification, as so as that on the module. Similar resuhs are obtained with different set of rotating speeds in the range 550-2700 rpm. The comparison between the theoretical response of the model to the identified faults and the experimental data is reported in Figure 5 for bearing #2 and in Figure 6 for bearing #3. ^ .|g6 Measuring Plane 3 - Vertical & Horizontal Directions - 1x Rev. Component
Measuring Plane 2 - Vertical & Horizontal Directions - 1x Rev. Component I —Experimental vertical —Analytical vertical ••"Experimental horizontal I ---Analytical horizontal
QI l l * ' V l f l ^ l l l l l l 600 800 1000
Ijilj
t'- 1
*v»
Vv--
1200 1400 Rotating
::V :
2200
g
::5 =:: • !^ :•:: 5:: j i l l J ^ / ^'%••. -. ?. ^Vy^ :si:s:j ':: :: : E ^-4f.^L)^
j^{aXv,nd}
(1)
where f.A^^, ^^ are rotation frequency, amplitude and phase, respectively, n is the number of the sample and superscript set {a,h,v,nd} means that measurement was made in axial, horizontal, vertical and non-driven end radial direction, respectively. Traditionally Fourier-transform based method (periodogram) has been applied to sinusoidal parameter estimation mainly due to its low computational complexity and lack of a priori assumptions on the signal. However, the Fourier method suffer from low resolution and high variance. The low resolution implies the use of long measurements. To improve the statistical properties of the periodogram an averaged periodograms can be used, but then even longer measurements are needed. The accuracy of the Fourier-methods is limited by the fact that during the long measurement period the rotating frequency can change significantly. To solve the problem of low resolution, high-resolution parametric methods have been developed, Kay (1988) & Marple (1987). of which the maximumlikelihood method is considered to be optional. We have developed a maximum-likelihood timedomain method, Saarinen & Orkisz (2001), to estimate accurately the rotating frequency from short measurement signal. After the rotating frequency is estimated, the amplitudes .4„' and phases ^,/, can be estimated easily by least square method , Kay (1988). The important characteristics with respect to classification of the faults are the mutual ratios of the velocity amplitudes Fj =Ai/(2mnf). (2) Before classification we form a so-called feature vector by applying non-linear transformation and normalisation to the velocity amplitudes.
720
The classification consists of statistical orthogonal expansion (Principal Component Analysis , Jolliffe (1986)) followed by cluster analysis, Saarinen & Korendo (2001). The obvious aim of the before mentioned data pre-treatment is to fmd out mapping which maximises the between-cluster variation and minimises the within-cluster variation. Mechanical status evaluation Vibration based condition assessment is illustrated in Figure 3.
Alarm settings
Prineipal Components ClMSifieation
Other fauH: iioii*syiichroiioii$
WmAi eiassification
iBWi^i
Figure 3: Vibration based condition assessment in ARMADA^ '^''' The bearing condition evaluation is based on a time domain shock pulse analysis, Toukonen & Makkonen(1999). Electrical status evaluation In-use electrical status evaluation consists of side band identification to find out broken rotor bars (the comparison of the slip frequency sidebands in the current spectra to the line frequency related peak in the same spectra) and separate measurements (voltage, current) to check things like common mode currents, total harmonics distortion and bearing currents. Measurements are done according to ARMADA^"^^ templates and resuhs are provided automatically to the end user. Insulation status evaluation This test was developed by Pinto (2000) and it is based on the work of Goffaux (1978). The analysis is targeted to detect the presence of contamination in electrical machines by measuring the "Insulation resistance" and the "Polarization Index" of the machine. Measurements are commonly done to the machine in off-line state with a hand-driven or a motor-driven DC generator or a regulated electronic DC power source, commonly known as a "megger". Measurement values are then fitted to model parameters and then an interpretation is done with fuzzy rules. Output to the ARMADA^^^ user is: measurement quality - indicating the quality of measurement, general winding status, contamination (conductive (carbon, carbonised oil, soot), non721
conductive (oil), moisture), ageing, depolymerization, defects of insulation between corona shield and core, lack of cure, erosion of corona protection shield, ingress of contaminant into the winding, cracks at slot ends or looseness of coils, Paithankar (2000). Reporting ARMADA^*^^ has an "1-button" reporting where it is possible to use the pre-defined types of automatic reports, see Figure 4. If required, customised reports can be created and added to the existing library, and these new reports can be shared within the organisation. Reporting is a proprietary development of ABB based on the Odyssey database and standardised reports - reflects directly the company requirements in the field.
Figure 4: 1-button reporting (operation diagram)
FIELD TEST RESULTS Before letting the ARMADA^'^^ system to the ABB wide distribution we made testing. Controlled lab testing is the important issue when checking the functionality of the algorithms. In real life there are always enough interferences (mechanical & electrical) present that the testing of the functionality of any condition monitoring system must be done at the real environments. Totally we have evaluated vibration and bearings status for more than 150 motors (from 15 to 630 kW) and 90 pumps. The most of the tested equipment were on the factory floor (pulp & paper mills, steel plants, and power plants). We tried to validate results as well as possible either with the repair shop results or with the other condition monitoring systems. One of the main resuhs for vibration and bearings was that no false alarms were encountered. If ARMADA^^^ informed about some problem there always existed one, even if not exactly the one that ARMADA^"^^ pinpointed. If there was any fault that ARMADA^ "^"^ was not able to notice we cannot be 100% sure. But those faults were also impossible to llnd with any other tools available. The insulation tests have been done before the concept of ARMADA^*^^ was invented. Hundreds of motors have been measured during the last ten years.
722
Our goal was to have three quarters of the fault cases classified automatically and according to results it seems we have reached it. The speed measurement from the vibration signal has proved to be reliable and accurate when the initial information provided has been reasonable (given nominal speed within ± 20% of the operating speed, number of poles of the motor). The error of the speed compared to the value of the tachometer is below one per mille. But there are still some problems in ARMADA'"'^'^ to notice when the initial information is misguided. INTEGRATION TO ABB'S WORLD WIDE MAINTENANCE BUSINESS ARMADA^"^^ is the result of intense co-operation between ABB Corporate Research Centers in Poland and Finland, ABB Lenzohm Service India and several ABB Service companies around the world. ABB's service business has created a structure to maximise the added value of the ARMADA ^ \ The common measurement templates enable the possibility of data and information sharing and immediate analysing help via Lotus Notes database, see Figure 5.
Aitil ABB $frv!G» Wortdwide
j,,,.,,,..,..,,..^^
,; 0«iirSpi?is|^#
:^^s^,
g^^p||>||:|
W^^^H
WW^KBIWi
Figure 5: Lotus Notes database for ABB wide sharing of the ARM AD A^^'"^ info
EXPERIENCES FROM THE FACTORY FLOORS There are now over twenty installations of ARMADA^^^ in several ABB locations. The first installations were done a year ago. One third of the installations have been in the "real use". The response to integrated and modular concept of ARMADA^^^ has been good. Traditionally there have been too many different tools. Automatic report generation that produces standard Microsoft Word files is valued high. The main source of dissatisfaction has been the amount of data needed to be stored in the data collector. ARMADA^^^ requires saved time domain data for automatic analysis (instead of spectrum data) and that demands a lot of memory in data collectors. Also transferring that data from the collector takes longer than in typical low-resolution spectrum based measurements. And finally there is still one shortcoming in ARMADA^"^^: Utilising it effectively requires training. But we feel the need is less than with other competitive systems.
723
CONCLUSION It is possible to build condition-monitoring tools that help the end user to concentrate on the cases that need special attention. The fast classification of machines to sound ones and "easy cases" frees human capacity to where it is really needed. ACKNOWLEDGEMENTS Our project team is thankful to many organisations and individuals that have made these developments possible. We wish to thank our customers: Mr. Amaldo Spiller from ABB Maintenance-TV and all the people working in the area of service within ABB. All trademarks are properties of their owners.
REFERENCES Goffaux R. (1978). On The Nature Of Dielectric Loss In High Voltage Insulation. IEEE Trans. Elect. Insul. Ei: 13, 8-11 Jolliffe I.T. (1986). Principal Component Analysis, Springer Verlag, USA Kay S.M. (1988). Modern Spectral Estimation: Theory and Application, Prentice-Hall, UK Marple Jr. S.J. (1987), Digital Spectral Analysis with Applications, Prentice-Hall, UK Paithankar A. and Pinto C. (2000). Fractal Analysis, A Novel Approach for Residual Life Assessment of Electrical Rotating Machines. Comadem 2000 proceedings Pinto C , Paithankar A. and Wnek M. (2000). Diagnosis of insulation health of rotating machines. CWIEME'2000 conference proceedings Saarinen K. and Orkisz M. (2001). Finnish Patent 20000646 Saarinen K. and Korendo Z. (2001). Polish Patent 338286 Toukonen J. and Makkonen A. (1999). Bearing Analyzer For Condition Monitoring Of Rolling Element Bearings Using Local Intelligence And Comprehensible User Interface. Comadem 1999 proceedings, Coxmoor, Oxford, UK
724
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
CONDITION MONITORING AND DIAGNOSIS OF ROTATING MACHINERY BY ORTHOGONAL EXPANSION OF VIBRATION SIGNAL T. Toyota', T. Niho\ P. Chen' and H. Komura^ 'Kyushu Institute of Technology, Kawazu 680-4, lizuka City, Fukuoka 820 -8502, Japan ^RION Company, Higashi motomachi 3-20-41,Kokubunji City, Tokyo 185-8533, Japan
ABSTRACT Here we present the new robust condition monitoring and diagnosis method based on the statistical hypothesis on the vibration characteristics of the rotating machines in good condition. The hypothesis is that if the machine is in good condition, its probability density function of the vibration signal follows the normal distribution in time domain. This method can lead to the robust failure diagnosis without any prior knowledge concerning to the vibration characteristics corresponding to specific failure to be detected. KEYWORDS Condition monitoring, Condition diagnostics. Vibration signal. Orthogonal expansion. Normalized power density function INTRODUCTION One of the widely used methods for failure detection and diagnosis of the rotating machines is one by vibration analysis. In this method, failure detection and diagnostic procedure can be roughly divided into four steps, namely; vibration measurement, signal processing, feature extraction, and feature recognition. So if we have little knowledge for vibration feature corresponding to the specific failures to be detected, it become difficult for us to detect and discriminate the malfunction or failure in the rotating machines. To solve this difficult problem, we set the one hypothesis on vibration characteristics of rotating machines in good condition. The hypothesis is that if the machine is in good condition, its density function of vibration signal follows the normal distribution in time domain. In this report, we propose the time domain analysis based on the hypothesis that if machine is in healthy condition, the density function of the vibration signal is distributed normally. 725
SURVEY OF THE CONVENTIONAL TECHNIQUES In condition monitoring and diagnosis by vibration analysis, as well known, time and frequency domain analysis techniques has been used. In the time domain analysis, we use the probability density function p{x) of vibration waveform to extract the features. Useful feature parameters in time domain analysis is skewness P^ and kurtosis 132, witch is defined by Eqns. 1 and 2. ^i-f_^{x-ixfp(x)dx
(1)
I32=j^jx-fifp(x)dx
(2)
Above two feature parameters, namely the skewness fi^ and the kurtosis ^^2 in time domain are very useful to extract the features of vibration signal for condition diagnosis. Typical research examples of the application of kurtosis P2 ^^ ^^e deterioration trend control in rolling element bearing. BASIC CONCEPT OF PROPOSED METHOD They show that kurtosis p^ ^^ ^^^Y indicative parameter of bearing's deterioration and its value is close to 3 if bearing is in good condition, and kurtosis P2 increase according to progress in deterioration. This fact suggest the proposed hypothesis is true, namely bearing is in good condition, its density function of vibration signal show that kurtosis value isP2 =3 and its density function is distributed normally. R.M. Stewart with Southampton University in U.K insist on that if bearing is in good condition, kurtosis value/32 show 3, and according to the progress of deterioration of bearing, P2 value increase and deviated from 3. This mean statistically that if bearing is in healthy condition, density function is normally distributed, and if condition become worse, its density function deviated from normal distribution. Author strongly insist that this hypothesis is true not only for vibration signal of bearing but also vibration signal of another rotating machines except the special structured rotating machines just as shown later section. Typical example for the first hypothesis are shown in Figure 1. Figure 1 (a) show the standardized vibration waveform of rolling element bearing in good condition. As you see clearly its density function is very close to normal density function. Figure 1 (b) show the standardized vibration waveform of rolling element bearing with local defects in outer-race. As you see clearly its density function is getting sharp and deviate largely from normal one. Figure 1 (c) show the standardized vibration waveform of rotating machines with unbalance. As you see clearly its wave form become sinusoidal and the density function is having two peak at both side. Basic concept of the proposed method is that measured pdf (probability density function) can be expanded into normal distribution component (good component) and residual components (bad component) by Gram-Charlier orthogonal expansion theory. By the Gram-Charlier orthogonal expansion theory (denoted by GC expansion in short), arbitrary pdf f{x) can be expanded into normal component q9(x)and its residual components. 726
0
0.2 pdf
Time
0.4
(a) Good condition
rft H
•o O.OS
H
ff
S -5.0 0 0.5 1 1.5 2 2.5 pdf
Time
(b) Local defect in outer-race t r
E
: :
MlWlWii^
CO
^
Measured pdf Normal pdf
O.OJ
ffi -5.0 0
0.2
Time
0.4 pdf
0.6
(c) Unbalanced condition Figure 1: Vibration waveforms and its density functions with different condition
f{x)==q{x){l+c^H^{x)+C2H2(x)+.. .+c„if„(x)+..} (3)
Here, q)(x) is density function of standardized normal distribution
cp(x)--
1
(4)
and Hfj (x) is the Hermite function of n-th degree. Expansion coefficient c^ can be shown by Hermite function //„ (x) c,=-f
f{x)H,Xx)dx
(5)
If pdf is standardized, C(, = l,c, ^c^ ^ 0, so Eqn. 6 reduce to f{x) = (p{x) + cp{x){c^H^{x) + c,H, {x) (6) = (p(x) + r{x)
727
Here r{x) is the deviation component from the normal distribution component ^, in Eqn. 1 and coefficients c^ is equivalent to the kurtosis p^ ^^ E^^- 2. For density function of vibration / ( x ) , let the mean //, = 3c ,and the variance cr^, r-th moments of about of the mean /u^ Advantages of GC expansion coefficients compared with conventional one are summarized bellow. 1) Measured pdf can be separated into normal components ^(x), that mean good component, and residual components r(x) ,that mean bad components, and values of coefficients c, is adjusted by /! automatically just seen in Eqn. 9. 2) Meaning of GC expansion coefficients c, are clear by Hermite polynomial, and by looking the value of c,, we can estimate the type of failures. 728
3) GC coefficients c. are orthogonal and its each coefficient is corresponding to different information of vibration signal, no overlapping in information.
ALGORITHM FOR THE FAILURE DETECTION AND IDENTIHCATION We can establish the failure detection and identification algorithm using GC expansion coefficients. Next algorithm can be used for failure detection and identification. for i s 3 for failure detection if V(Ci=0) then good condition if
3(Ci^0)
then bad condition
for failure identification if
Cj ^ 0
thencj type failure
if
Cj ?i 0 a n d Cj ^
0 then Cjj type failure
CRITERION FOR THE GC COEFFICIENTS AND DEFINITION OF THE SEVERITY FACTOR From the orthoganality of Hermite polymomial .- {f{x)- a ) /=3
729
(13)
-S
4.0|
I
2.0
I
0.0
1 -20MAMMJA' I -4.0 0.1
0.3
0.2
Time [sec] (a) Good condition l0.08
0.1 Oi
0.08 Q>
^0.06
0.06h
O
0.04
"5
0.04h
:ro
0.02
i0.02
0.00'
0.00
Coefficients of Gram-Charlier expansion (b) GC coefficients and T^ Figure 2: Vibration waveform and its GC coefficients for rolling element bearing in good condition
VERIFICATION OF THE EFFECTIVENESS BY EXPERIMENT Figure 2 show the vibration waveform measured from the bearing in good condition. Figure 3 (a) show the vibration waveform measured on rolling element bearing with small local defect on inner race, and by Figure 3 (b), we can confirm that all individual expansion coefficient C^ and severity factor T^ grow large and exceed the criterion values. Figure 4 (a) and (b) show the vibration waveform measured on rolling bearing with local defect on outer race. Also we can confirm that the individual expansion coefficient C/" and the severity factor grow very large and exceed the criterion values. APPLICATION TO THE DIAGNOSIS OF MACHINES FOR WHICH THE HYPOTHESIS IS NOT IN FORCE For some types of rotating machines, its density function of the vibration in good condition do not follow the normal distribution. Typical machines of this type are 1) Bladed machines Vibration signal of bladed machine such as compressors, pumps has blade passing frequency components, so its density function do not follow the normal distribution precisely. 2) Reciprocating machines Reciprocating machines has inherently unbalance component and its vibration signal have rotating frequency components and its harmonics. 3) Another special structured machines Machines such as the high seed ones with flexible shaft have the sinusoidal vibration waveforms and do not follow the normal distribution, in good condition.
730
0.1
0.2 Time [secj (a) Vibration waveform lO.OOi
15.00
10.00 X(3, 0.95)
5.00
0.00
Coefficients of Gram-Charlier expansion (b) GC coefficients and its T^ Figure 3: Vibration waveform and GC coefficients of bearing in initial failure condition
10.0]
•"I
Li.
: 11 rt
Lii
rt
0.0
S-10.0J
0.1
^'t]
0.3
0.2 Time [sec]
CO
(a) Vibration waveform 25.00I
i30.00
^ 20.00
i#?^
i 15.00I
20.00
o *5 10.001 ^
5.001
X(h 0.95)
10.00 ]X(3>0.95)
f^-l . ...
0.00
0.00' C^
C2
C3
C4
C5
T
Coefficients of Gram-Ciiarlier expansion
(b) GC coefficients and its J^ Figure 4: Vibration waveform and its GC coefficients T^ for dangerous condition For diagnosis of this kind of machinery, we can apply the presented method with a little modification of GC expansion coefficient. At first step, calculate GC expansion coefficients c^' of the vibration signal in good condition. Next step, make difference between measured GC coefficients c. and prepared coefficients c / , c^ " = Q -c^. ', this new coefficient c^ " can be used to diagnose the condition of these machines and have same advantage as original GC coefficients.
731
CONCLUSION We proposed the new method to detect and diagnose the rotating machinery condition based on the hypothesis that if the machine is in good condition, its density function of the vibration signal is distributed normally. 1) If machine is in good condition, all individual components q are zero 2) Individual coefficients c^ are corresponding to the specific failure type, so we can estimate the failure types by investigating c, that exceed the pre-decided criterion values. Another advantages of this method is 1) Criterion value for individual coefficients Q can be theoretically decide and its criterion value is always constant 2) Severity factor T^ that show the severity of failure can be defined theoretically and its criterion values are determined statistically. References Stuart A. and Ord K. (1994), Kendall's Advanced Theory of Statistics, Volume 1, Griffin. Stewart R. M. (1979), Machinery Health Monitoring Group Technical Brochure, University of Southampton. Stewart R. M. (1977), Some Useful Data Analysis Techniques for Gear Box Diagnostics, Institute of Sound and Vibration, University of Southampton.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
COMPARISON OF APPROACHES TO PROCESS AND SENSOR FAULT DETECTION A. Adgar. Control Systems Centre, University of Sunderland, School of Computing, Engineering & Technology, Edinburgh Building, Chester Road, Sunderland, SR13SD,UK ABSTRACT Many process industries are under continual commercial and environmental pressure to produce higher quality products at lower cost. Improved process control through the introduction of new technologies is an area of prime importance in the drive towards these goals. With many industrial processes becoming increasingly more complex, highly instrumented and using more advance control strategies, it is necessary to increase investment in process and sensor fault detection schemes to ensure the process may be monitored and thus controlled in a safe and efficient manner. The water industry is not immune to any of the problems mentioned earlier, and indeed it has many problems specific to itself The reduction in staff levels has made routine maintenance of the sensors required for control of the process impractical. A condition based approach is now necessary. The techniques to be described exploit a range of techniques utilising statistical models, neural network models and fuzzy models to represent knowledge about the process. This can be captured in terms of the normal variance of a particular sensor and its relationship to other sensors under a set of operating conditions. Faulty sensor measurements, once detected, can be reconstructed based upon the fault detection models described earher. The fault analysis process will continue to function and by using the other measurements can produce an accurate estimate of the corrupted sensor measurement. Particular emphasis will be placed on the pre-processing of data sets, and the best ways in which to introduce examples of fault scenarios into the data used for model development. Computational time required for the different model types will be considered as well as their overall performance and drawbacks. The effectiveness of these new techniques will be demonstrated using data sets collected from a real treatment works, with fault information superimposed upon it.
KEYWORDS Artificial neural networks, multivariate statistics.
fuzzy modelling,
fault detection,
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input selection,
water treatment,
INTRODUCTION The majority of the drinking water consumed in the UK is treated at surface water treatment works where raw water is abstracted from rivers and reservoirs. The type of treatment it then undergoes depends on the source and the quahty of its water. In general, the poorer the qualities of the raw water the more expensive it is to treat. The operation of water treatment plants is significantly different from other 'manufacturing' industrial operations because the raw water sources are usually subject to natural perturbations. This is especially true during periods of flood and drought that significantly affect the characteristics of the water abstracted into the treatment works. Prior to privatisation the majority of water treatment processes were under manual control. Since privatisation, the water industry has been seeking ways, especially via the increased use of automatic control [Bevan, 1999], to produce high quality water at reduced cost whilst at the same time 'downsizing' its work force. The water treatment process consists of a complex group of interconnected physical and chemical systems. It is not immediately obvious how each one relates with its neighbour, however, it is well known that a problem with one process, if not addressed, will quickly result in a much larger problem in one or more of the subsequent stages. Water treatment plant operators now have a wealth of modem instruments available as aids for monitoring the performance of chemical coagulants. Such instruments include zeta-potential meters, streaming current detectors and coagulant residual analysers. The instruments, however are always at the mercy of sensor failures and this causes problems for control schemes which act upon the sensed variables. Sensor failures may occur due to several physical reasons including: bubble formation on electrodes, fouling of electrodes by solids or chemical species in the water and also clogging of instrument sample lines and chambers.
OVERVIEW OF WATER TREATMENT The purification of water for domestic consumption involves several stages of treatment of the raw water to remove suspended solids, colour and bacteria before entering the distribution network. The individual treatment processes include clarification, disinfection, pH adjustment, filtration and taste and odour removal. Some of these are presented in a typical treatment scenario in Figure 1.
Coagulant
Figure 1: Water treatment process schematic diagram.
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Raw water is often stored prior to treatment to improve the water quality (by settlement, bleaching by UV light and oxidation) and also to ensure adequate supplies at periods of high demand. The storage units may also attenuate the quality variations in the raw water. Screening may also be performed. Here, the inlet water is passed through a grid of bars (for coarse screening) or through floating booms or air bubble curtains (for finer screening.). Clarification can be roughly divided into a two-stage process, comprising of coagulation and flocculation. In the coagulation stage, a coagulant chemical (a salt of a highly charged metal ion) is added to the water in a mixing vessel, often with mechanical agitation to ensure uniform distribution of the chemical. The highly charged metal ion destabilises the negatively charged impurities in the water. The species combine together to form larger particles called floes. Flocculation involves the combination, by collision, of small particles, under natural turbulence, into larger particles. A flocculation aid (usually a natural starch or a polymer solution) may also be added to assist floe formation. The final stage of the clarification process is the separation of the large floes formed by coagulation and flocculation from the water, usually by settlement. Water flows upwards in a tank, which may be of uniform or increasing cross section. The balance between the settling velocity of the floes and the up-flow rate of the water allows the floes to be held in a 'blanket' of sludge in the tank. This 'blanket' traps other floes and becomes more concentrated. Clean water is decanted fi*om the top of the tank over weirs. The amount of sludge present may be controlled by periodic removal from collection hoppers in the blanket. Filtration is used to remove small grade suspended matter from the water. The water is passed through layers of sand, gravel and anthracite. Pipes buried in the layers of sand collect the filtered water. Particles trapped by the layers of sand must be periodically removed by 'back-washing' (forcing air and/or water under pressure back through the bed, temporarily fluidising it and removing trapped impurities.) Filtered water usually contains some remaining bacteria and pathogenic viruses. These are removed before the water goes to supply, by disinfection using ozonation, chlorination or UV radiation. Li the UK chlorination is the method usually applied mainly because chlorine is easy to add to water, is highly soluble and cheap. Water treatment processes, such as clarification and filtration produce considerable amounts of waste sludge in the form of slurry. This is usually held in holding tanks or lagoons to allow settlement and then further treated in filter presses. The concentrated sludge is disposed at landfill sites and the supernatant water may be returned to the inlet of the works for re-treatment.
FACTORS LIMITING SUCCESSFUL PROCESS CONTROL SCHEMES Early applications of automatic control in the water industry were compromised by the poor quality of instrumentation. Improved sensor technology enabled successful regulation of variables such as pH and chlorine concentration. However it is the control of the clarification and filtration stages, which is fundamental to the efficient operation of a treatment plant. This remains to be successfully accomplished at many treatment works. The design of effective feedback control schemes for the latter systems is difficult for two primary reasons. Firstly, instrumentation measuring the performance of the units is only now beginning to emerge. Their reliability and accuracy, however is not always of sufficient standard to achieve quahty objectives and the plant has long time constants, has ill defined, non-linear characteristics and varying process dead-times. These factors render the application of control extremely demanding. Nevertheless, with the advent of low cost modem computing power, coupled with advanced modelling and control techniques, it has now become feasible to reduce many of the sensor limitations and process difficulties.
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More recently the use of streaming current detectors for simple clarification feedback control has resulted in major cost savings on a typical water treatment plant [Bishop, 1992; Dentel et al, 1989]. Considerable benefits have been obtained, but the instruments are far from reliable and a high level of maintenance is required. Current water treatment plant monitoring and control methodologies are site dependent, somewhat antiquated and still labour intensive (even after considerable downsizing). As described in the introduction, this paper outlines some of the research carried out at the University of Sunderland to resolve some of the problems relating to sensor failure scenarios. A PLANT MONITORING AND DIAGNOSIS STRATEGY Previous work by Adgar & Cox [1997] and Bohme et al [1998] has suggested possible strategies to detect and identify sensors that have failed or are beginning to fail. These range from simple univariate statistical models through more complex multivariate statistical models and finally to advanced techniques such as the auto-associative neural network. Fault Detection Faults occurring on sensors are detected by using specific previously determined fault detection models. In this research models have been constructed via linear regression, fiizzy logic and neural network approaches. The model development has been performed in each case by taking representative trains of process operating data and superimposing fault characteristics on the data by the injection of noise into the process variables. In each case, training, validation and test data sets were utilised in a ratio of 40%, 40% and 20% respectively. In each case, local sensor fault detection modules are developed to estimate *true' values of specific process variables even when faults may be present in other signals. Simulink interface One of the difficulties encountered when researching this type of problem is that the data is difficult to handle. The faults introduced into the operating data have been simulated, but we need a method by which we can examine the performance of the sensor fault detection schemes under a wide range of condidons. The different model types should ideally be tested on a selection of operational data sets, the fault types and magnitudes should be varied, as should the time of introduction of the faults. This requires a sophisticated and flexible development environment not ideally suited to conventional programming techniques. The author has found the Simulink environment to be suitable for these purposes. The simulations can be set up in a drag-and-drop model structure (see Figure 2) to allow rapid changes to be made to the model test schedules.
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RESULTS The summary of performance of the three model types are shown in Table 1. As can be observed, the model accuracy increases (the sum-squared-error decreases) with model structure complexity, as expected. Table 1: Model errors for training, validation and test data sets. Model Linear Fuzzy Logic Neural Network
Training 0.5309 0.5144 0.3843
Validation 0.5166 0.4951 0.4238
Test 0.4871 0.4654 0.3855
The residual errors of the three models are shown in Figure 3 in histogram form. Again this reveals the smaller error variance attributed to the more sophisticated models. An example of the detection scheme results in practice is shown in Figure 4, with the linear fault detection model being used. Here we have simulated a sensor drift added to the first process variable at sample time 220. The faulty (measured) signal, true signal and on-line estimate are shown in the top graph. The estimate can be seen to be very close to the true signal at most times. The lower graph in Figure 4 shows the simulated fault, the error between the on-line estimate and the measured signal and a filtered version of this error. As the fault magnitude increases, the corresponding error increases and this can be used as a fault detection mechanism.
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400 r a) Linear Regression Model
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The proposed sensor validation scheme is fairly sensitive to fault impacts on each sensor variable and is ideal for detecting 'soft' failures. However, the network does not always work on the same variable fault magnitudes. The levels of fault which can be detected depend upon the levels of noise used when pre-processing the data. High levels of noise will give good fault detection performance, but may reduce signal reconstruction accuracy. One major advantage of this data-driven technique is that there is no need to have detailed knowledge about the system.
CONCLUSIONS The introduction of increasingly stringent regulations at water treatment works emphasises the need for a high degree of reliability in the operation of the individual unit operations. The response of the industry has been the estabUshment of a range of quality or conformance standards since their satisfaction offers potential benefits in terms of production consistency, reduced operational costs and improved safety. However, these improvements can only be realised if the process control and plant management policies are well structured and designed. This paper has identified a number of situations where an ANN has been used to help provide a solution that has contributed to an improvement in the overall plant performance. Control strategies are always at the mercy of sensor failures or malfunction. Any means by which these can be dealt with effectively are of great value. Benefits include the security of treatment operations and cost effective performance. Methodologies which identify sensor failure and aid signal reconstruction have been described. The results suggest that the vahdation technique is able to adequately identify sensor failure whilst also providing reasonable estimates of "corrupted measurements" for monitoring and control purposes.
ACKNOWLEDGEMENTS The authors would like to thank University of Sunderland and Northumbrian-Lyonnaise for the technical and financial support for this work.
REFERENCES Adgar, A. & Cox, C.S. (1997) Improving Data Reliability using Statistical and Artificial Network Strategies, COMADEM'97, 10th Int. Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management - Espoo, Finland, 2, 94-101, 9-11 June, 1997. Bevan, D. (1999) Monitoring, Control and reporting strategies for surface water treatment plants M.Phil. Thesis, University of Sunderland. Bishop, S. (1992) Use of the streaming current detector at Langsett Water Treatment Works. Journal of the Institution of Water & Environment Management, 6:1, 1-9. Bohme, T.J. et al (1998) Sensor failure detection and signal reconstruction using auto-associative neural networks. Int. ICSC/IFAC Symp. Neural Computation, Vienna, Austria, Sept. 1998. Dentel, S.K. et al. (1989) Evaluation of the streaming current detector, I. Use in jar tests. Water Research, 23:4, 413-421.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE NEURAL NETWORK PREDICTION OF DIESEL ENGINE SMOKE EMISSION FROM ROUTINE ENGINE OPERATING PARAMETERS OF AN OPERATING ROAD VEHICLE E.Berry, J.Wright, P.Kukla, F.Gu and A.D.Bali Maintenance Engineering Research Group The University of Manchester Oxford Road, Manchester, United Kingdom, Ml3 9PL Email:
[email protected] Phone:+44 (0)161 275 4407 Web: www.maintenanceengineering.com
ABSTRACT Accurate measurement of diesel exhaust smoke is a primary phase in meeting the ever-stricter EC regulations on emission levels and a fundamental step towards the improvement of many factors including fuel economy, atmospheric pollution levels and more importantly, human health, with the additional aim of automatic engine management systems and condition based maintenance. However, it is often difficult to measure smoke levels directly on a vehicle in transit, therefore this paper documents a study into the feasibility of diesel exhaust smoke prediction based upon the engine operating parameters of exhaust temperature (°C), engine speed (rpm) and road speed (mph) using a hybrid neural model. The results show that the smoke can be predicted by indirect measurements with good accuracy. KEYWORDS Diesel particulates, neural networks, emission prediction, routine operating parameters. INTRODUCTION It is well understood that the monitoring and analysis of exhaust smoke generated by a diesel engine can provide powerftil information about a range of factors, from the condition of top-end engine components, through the human health implications of the emission, to the environmental impact that the operation of the engine is having. The simplest basis for such an assessment is merely quantitative. More complex analyses target, amongst other things, the chemical composition of the exhaust particulate and the distribution of particulate size. Direct measurement of diesel engine exhaust smoke output is difficult to perform for an engine operating in a vehicle because of the equipment involved
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and access restrictions. For this reason the possibilities for direct measurement are generally restricted to test-bay usage. The ability to infer smoke emission level from routinely monitored engine operating parameters, without the need for specialist or cumbersome instrumentation, would bring with it the possibility of mapping quantitative particulate emission for an engine in a vehicle operating under normal on-road conditions. This, in turn, would provide the basis for a whole spectrum of possible benefits, from the optimisation of fleet vehicle deployment according to the duty cycles of specific routes, to the automated real-time adjustment of engine operating parameters based upon predicted pollutant emission. This paper outlines the next step in the work carried out by Berry et al (May 2001), which involved recording data from a test bed engine. The work documents the progression to a vehicle on the road. A correlation between easily measurable engine parameters and the exhaust emission levels by using them in a neural network model is investigated. Previous work in thisfieldincludes estimating diesel engine pollutants using a regression model based on engine speed, air-flow andftiel-flowby Ouenou-Gamo et al (1998). MEASUREMENTS AND DATA SETS The test data was recorded from a 10.5 litre, V8 Perkins 640 diesel engine powering a Dennis fire appliance. The raw data acquired during the test is illustrated in Figure 1 below. The vehicle was driven over varying road gradients in order to achieve afiiUrange of typical loads and speeds. The exhaust temperature, engine speed and road speed were recorded throughout the test. This data was used as the input parameters for the prediction. Along with the engine operating conditions, the smoke emission in Filter Smoke Number (FSN) was measured simultaneously.
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Figure 2 shows the basic trends of the engine emission with exhaust temperature, engine speed and road speed. It indicates, with exhaust temperature in particular, that the emission increases as the value of the measured parameter increases. However, the emission is correlated to the measured parameters in a non-linear way, and hence a neural network is employed to model this relationship.
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NEURAL NETWORK MODEL (NNM) A generalised regression neural network (GRNN), Demouthe & Beale (1997) & Wasserman (1993) was used to model the non-linear relationship between emission and operating parameter. The GRNN used had two layers of neurons, with the first layer being radial basis functions and the second layer comprised of linear neurons. The data set consisted of 152 samples, so with half of the data being used for network training and the other half for testing, the first layer had 76 radial neurons, and correspondingly the second layer also had 76 neurons. The spread of radial basis functions in a GRNN isfixedby a spread parameter. This controls the tradeoff between over-fitting and under-fitting. Each different configuration of GRNN was optimised to find the spread parameter giving the least error. Figure 3 below shows how the error between the predicted and raw data varies with a varying spread parameter. GRNN optimisation
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Figure 3: Network optimisation Legend o: 1-input network using exhaust temperature x: 2-input network using exhaust temperature and engine speed *:: 2-input network using exhaust temperature and rroad speed 0: 3-input network using exhaust temperature, road speed speed;and engine speed It can be seen clearly in Figure 3 that the more inputs the network uses, the lower the average error produced. Seven predictions have been carried out, i.e. every combination of the three engine parameters recorded. The first was performed using all three engine parameters, namely exhaust temperature (°C), engine speed (rpm) and road speed (mph). The network was then 'pruned' to omit either one or two of the parameters and a prediction carried out again using the same raw data.
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RESULTS AND DISCUSSION Figure 4 gives an indication of how well the NNM predicts the smoke emission using all three parameters. It can be seen that the network produces good prediction results for both the training data set and test data set. This means that the network has not only fitted well to the training data but that it also has a good generalisation capability.
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Figure 4: Network emission predictions for the training and test data sets using exhaust temperature, engine speed and road speed. Figure 2 suggests that, out of the three parameters, exhaust temperature has the closest relationship to emission. It was therefore concluded that the initial pruning of the network should be done by omitting engine speed and road speed. Figure 5, therefore, shows the network test error when using a combination of all three parameters as inputs to the network, with exhaust temperature as a common input in all four. The lowest average error suggests that this network can predict to within 0.12 FSN using exhaust temperature, engine speed and road speed as it input parameters. It can therefore be concluded that the configuration of the network is good enough for application.
745
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Figure 5: Error analysis using: (a) exhaust temperature, engine speed and road speed as input parameters, (b) exhaust temperature and road speed as input parameters, (c) exhaust temperature and engine speed as input parameters and (d) exhaust temperature as the only input parameter. Figures 5 (b) and (c) show the network test error of a two-input NNM after having omitted the engine speed then the road speed respectively. Figure 5 (d) shows the error of a single-input NNM after both engine and road speeds were omitted. It can be seen that the average network test errors do not vary much compared to the un-pruned network shown in Figure 5 (a), nevertheless the error does increase.
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CONCLUSIONS AND FUTURE WORK The work in this paper has demonstrated that routine engine operating data can be fitted by a neural network, and that this can then be used to give a close prediction of exhaust smoke output. It has also shown that a two input network displays a fairly equal capability when predicting emission levels as when all three parameters were used. Although at an early stage, this work highlights the feasibility of indirect measurement of engine emissions on a moving vehicle with a view to automatic engine management and condition-based maintenance. The next stage of the work will involve advanced optimisation of a network using the least amount of inputs for easy application to a road vehicle. Investigations are currently being undertaken into the effect of using signals from more recent engine sensors such as manifold absolute pressure and throttle position for use in the NNM. In some ways, this work can be considered better than test bed experiments, due to the exposure of the test vehicle to erratic 'real world' conditions, for example humidity and atmospheric temperature and pressure. REFERENCES Berry E., Wright J., Kukla P., Gu F., Ball A. (May 2001). The Prediction of Diesel Engine Smoke EmissionfromRoutine Engine Operating Parameters, and its Implicationsfi)rEngine Health Monitoring, MARCON Conference, Gatlinburg, TN, USA Demouthe H. and Beale M. (1997). Neural Network Toolbox User's Guide, MathWorks Ouenou-Gamon S., Ouladsine M., Rachid A. (1998). Measurement and prediction ofdiesel engine exhaust emissions, Elsevier Wasserman P.D. (1993). Advanced Methods in Neural Computing, Van Nostrand Reinhold
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
EARLY DETECTION OF LEAKAGE IN RECIPROCATING COMPRESSOR VALVES USING VIBRATION AND ACOUSTIC CONTINUOUS WAVELET FEATURES M. Elhaj, F. Gu, A. D. Ball, Z. Shi, J. Wright Maintenance Engineering Research Group, University of Manchester Oxford Road, Manchester, Ml3 9PL, UK www.maintenance.org.uk Phone+44 (0)161-275 4541
ABSTRACT Valve leakage is a major source of failure in reciprocating compressors and greatly influences the operation and performance. Valve impact and non-stationary airflow induction are two primary sources of vibration and acoustics. Therefore, this paper investigates the early detection of valve leakage based on vibration and acoustic measurements. It was revealed that the conventional analysis in either the time or frequency domain can not resolve the detection information from the acoustic signal due to noise contamination. The joint time-frequency analysis of the Continuous Wavelet Transform (CWT), however, can extract fault detection features successfully. These acoustic and vibration CWT features were developed for small leakage in both the suction and discharge valves of the compressor. Compared with vibration monitoring, acoustic monitoring needs more signal processing but its implementation can be carried out remotely. KEYWORDS Compressor Valves, Vibration Monitoring, Acoustic Monitoring, Continuous Wavelet Transform, Fault detection. INTRODUCTION The most common failure in reciprocating compressors is leaky valves. Not only the fault reduces performance of compressor but also causes secondary damage to other parts of the compressor. In [6, 9], valve problems were concerned as the primary cause for reciprocating compressor shutdown. Figure 1 illustrates that such damage accounts for 31% of complete breakdowns on this type of compressor and represents the biggest source of failure [6]. The leakage of valve results in a loss in efficiency because air is simply being pushed forth and back across the valve. Figure 2 gives the performance graph of a compressor obtained by measuring the discharge timing at different discharge 749
pressures. The discharge time needed for different valve leakage is longer than that of normal, which indicates that the compressor has to w^ork harder [2,3]. Leaky valves can result from damaged channels or valve seats or warped channels due to high process temperatures. In addition, carbon build-up can occur because of the high temperatures that can be reached at discharge which (sometimes over 200 C ) tends to bum the oil that accumulates on the channel and valve seat. Leaks tend to occur more often in the discharge valve because of the higher impact velocities and temperatures that are reached there. Also, leakage of the discharge valve is more severe than that of the inlet valve for the following reasons: the opening time of the discharge valve is shorter than that of the inlet valve, hence, the leakage time is greater: the average sealing pressure is higher: and the heat strain reduces sealing ability [2,3]. Due to the valve leakage, the temperature inside cylinder and valve system will increase further and hence accelerate the deterioration of whole system. Therefore, many techniques have been investigated to detect valve leakage. Although vibration analysis has been widely used for the monitoring of machines such as bearings, gearboxes and gas turbines, it still proves difficult to successftilly apply this to reciprocating compressors because they are naturally noisy, heavily vibrating machines. Problems are also encountered due to the non-stationary characteristics of the vibration signals encountered in this type of machine [7]. Discharge and suction valve leakage can reduce compressor efficiency.
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Figure 1: Failure rates in reciprocating compressors [6] This paper investigates the early detection of leakage and more generally the problem of predictive diagnosis in the discharge and suction valves of reciprocating compressors. Analytical and experimental studies of the vibration response of a reciprocating compressor have led to the development of a procedure for the detection and diagnosis of valve faults. In particular, we have found that timing and strength of the valve impacts are directly related to the severity of valve leakage. In reciprocating compressor valve operation generates transient vibrations and sounds which are so broad in frequency that vibration spectra tend to be very complicated, a fact which can render conventional vibration monitoring practically useless for detailed fauU diagnosis of valves. Therefore, the joint time and frequency analysis of the continuous wavelet transform (CWT) representations offers a solution and provide diagnostic information for valve faults [1].
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P r * « s t i r « (PSlJ
Figure 2. Influence of leaky valves on compressor performance VIBRATION AND ACOUSTIC SOURCES OF A RECIPROCATING COMPRESSOR Many studies have shown that the major source of vibration and noise are the mechanical impacts between valve plate and its seats. In addition, the sequences of airflow and non-flow processes, which constitute a cycle in the compressor, generate gas pulsation in the system. This non-stationery airflow also causes vibration and noise [5]. I-Mechanical impacts of Compressor Valve
^ 1 Generally, the suction and discharge valves in a reciprocating compressor are of similar design, containing a valve plate, a spring and a pneumatic chamber. As illustrated in figure (3), a compressor valve can be modelled with a mass, a spring and a damper. The movement of the valve plate is thus governed by a non-linear process [1,4] and forms a sequence of events: the rapid opening of the valve, linear move of the valve and impacts between the valve and seats. The amplitude of vibration and noise response measured close to the valve can be characterised as follows: - Higher discharge pressure [Pa ] produces higher amplitudes; - Higher cylinder pressure [Pc] produces higher amplitudes; - Smaller mass [m] of the valve plate produces higher amplitudes; - Lower stiffness [k] of the valve spring produces higher amplitudes; - Lower damping [C] of the valve chamber produces higher amplitudes. //- Non-stationary Airflow
A
\//A
1
jm 4
1
V
Figure 3: The dynamic model of a compressor valve
P'^ure 4: Airflow path within reciprocating compressor valve
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Figure 4 shows the airflow inside a reciprocating compressor valve. The airflow starts from the cylinder chamber, travels through the cylinder port and valve chamber and finally reaches to the delivery tube. For the suction process, the airflow path is the exact opposite to that of the discharge process. As the velocity and pressure of this airflow is varying with time, it may be concerned as secondary sources of vibration and sound. The sound generation may be quite similar to the way humans form recognisable speech from a bit of air pressure and tissue motion [1,8]. This indicates that the sound and vibration produced from the airflow is not only determined by the pressure of airflow, but also influenced by the parameters of the airflow paths. Therefore, the changes in the chambers, valve plates, springs will lead to a variation in measured sound and vibrations. In general the vibration and sound from reciprocation compressor are a complicated combination of mechanical impact and non-stationery airflow. CHARACTERISTICS OF THE VIBRATION & ACOUSTICS IN A RECIPROCATING COMPRESSOR A two-stage (Broom-Wade Reciprocating Compressor -TS9) was used for the study of valve fault detection using vibration and acoustic methods. An accelerometer was installed upon the cylinder head. As the temperature of the cylinder head can reach as high as 120°C and limited space is available; a transducer adapter is glued to the cylinder to reduce the heat transferring to the accelerometer. In addition, the temperature of the cylinder surface was controlled at around 90**C to reduce temperature-varying errors on the accelerometer. In contrast, no treatment is required when a microphone is installed 10cm away from the cylinder. In order to validate previous analysis, this study of fault detection was carried out using a leaky discharge and suction valves plates on the first and second stage cylinders. Figure 5a shows the raw vibration signals. There are four significant transient vibration responses within one rotation period of the compressor cycle. These four transient responses are well consistent with valve impact events: Suction Open (SO), Suction Close (SC), Discharge Open (DO) and Discharge Closing (DC). When the top dead centre (TDC) of the piston in the first stage cylinder is taken as the reference position, the sequence of the four events for the second stage will be identified as show in the figures. In addition, the discharge event related vibration is higher than that of suction events. This is due to that the vibration transducer is closer to the discharge valve. However, due to low frequency noise influences, the four events can not be observed fully from acoustic signals shown in Figure 5bl. Only the opening of the suction valve of the first stage can be seen around 50° with higher amplitudes.
FAULT FEATURE IN TIME DOMAIN To study the vibration and acoustic fault features, a Imm-diameter hole is drilled in valve plates to simulate the valve leakage. As indicated by the performance plot in Figure 2, the influences of these leakage for the second stage on the performance is very small (less than 8% at the maximum discharge pressure). Without any post-processing, the vibration signals measured on the compressor cylinder head in are shown in Figure 5a. Some difference can be seen between the faulty and normal cases. From the variation of crank angle position of the impact events, the leak causes the advanced opening of the discharge valve and the reduced opening impact strength. The reason for this is that high pressure air flows back though the discharge leak and raises the pressure in the cylinder above that which would 752
normally exist. As a result, the pressure needed to open the valve is reached sooner, though the impact severity changes slightly. The suction valve is also altered because the higher cylinder pressure. It delays the time at which the pressure is low enough to open the valve. Also, the final closing impact for the suction valve occurs slightly earlier.
4a I
100
150
I 200
2S0
channels DischargB Pr«8Sure=120Se|men1=2
100
3C|I
0
Figure 5 a: Raw vibration signal of discharge valve leak
Figure 5c: Raw acoustic signal of discharge valve leak
150
,200
2fO
: ^
3S0
chsnnaf 2 Discharge Proasur«=120So||menl'"2f
i
SO
100
ISO
200
250
300
350
Figure 5b: Raw vibration signal of suction valve leak
Figure 5d: Raw acoustic signal of suction valve leak
When the leakage occurs in a suction valve, the suction valve will open earlier due to the clearance volume of gas expanding on the downward stroke of the piston. There will be less pressure than normal during this stroke and the pressure differential across the suction valve will become sufficient to open the valve at an earlier point in time. In addition, this delays the closure of the suction valve as shown infig6b. Also figures 6c, and 6d shown the four events are not reflected directly in the time-domain of the acoustic signal of suction valve leaky, high noise content, and the fault is not clear as shown in vibration signal. FAULT FEATURES IN THE FREQUENCY DOMAIN Fig 6a and 6b present the spectrum results of both vibration and acoustic signals for discharge and suction valve signals. Comparing the spectra between healthy and faulty cases, the vibration spectrum shows enough differences for the detection of the faults. Although the acoustic spectrum also shows some difference but not as distinct as that of vibration. Either time or frequency method along reveals a limited amount of information from acoustic signals. This calls for an alternative analysis technique.
753
000
laooo
Figure 6a: Spectral differences for vibration and acoustics of the discharge valve Pr««sura-120
Healthy "••[
1
y^
jjjl
tooo
i3oac
L^wfii
1
y LJ,J
T^i^Mmmmmkmi^m %S^
Figure 6b: Spectral differences for vibration and acoustics of the suction valve FAULT FEATURES IN THE TIME-FREQUENCY DOMAIN Figures 7a to7d present the time-frequency analysis results of the vibration and acoustic signals. The Continuous Wavelet Transform (CWT) provides the information jointed in bothfrequencyand time. This then allows the development of more detailed fault features in both domains together, which can be used for both detection and diagnosis. Comparing the two types of mesh graphs between faulty and healthy of the discharge and suction leakage, the fault features can be clearly observed. The variation in impact magnitudes for opening and closing in the faulty CWT representation is minimal when valves open, and slightly higher when valves close. Also evident is the advanced opening and retarded closing of the discharge valve. Earlier closing of suction valve is also shovm in these results. If leakage occurs in a suction valve, the valve will open earlier and close later. The amplitude is slightly higher when the valves close due to higher cylinder pressure.
ono 10000 12000 Crarxc angi* (deg^
Crsmr angiA (cteg)
Figure 7a: CWT representation of vibration signals for the discharge valve
754
j ^ ^
jW5i ^
r—
0
50
100
150 200 Crank angle (deg)
250
300
350
Q
I
;
I
SQ
IOO
I
;
;
; I
150 200 Crank angle (deg)
Healthy
250
200
150
100
60 Crank arrgle (cteg)
Crar* angle (deg)
Figure 7b: CWT representation of acoustic signals for the discharge valve
Crank ang^B (cjegi)
Crank angle (oteg)
Figure 7c: CWT representation of vibration signals for the suction valve
. 8000 10000 12000 Crank angle (deg)
Figure 7d: CWT representation of acoustic signals for the suction valve
755
2000 4000 . ^ -^
CONCLUSIONS AND FURTHER WORK This paper investigates the early detection of valve leakage based on vibration and acoustic measurements. To obtain the signatures for both the detection and diagnosis, the joint time-frequency analysis of the Continuous Wavelet Transform (CWT) has to be applied to the acoustic signals. Acoustic feature revealed by CWT is as clear as thosefromvibration signals and can be summaries as follows: • For the leakage of the discharge valve, the discharge opening and the suction closing are advanced while the discharge closing and the suction opening are delayed. The amplitude of the faulty vibration is also larger. • For the leakage of the suction valve, the suction closing and the suction opening are advanced while the discharge events has less variation in time. As the surface temperature of the cylinder head is high and the transducer mounting space is compact, the acoustic monitoring thus can be taken an alternative to the vibration monitoring. REFERENCES [1]. B. Liang, F.Gu, A.D. Ball. (1996). Valve Fault Diagnosis in Reciprocating Compressors, Maintenance, Volume 11, Number 2, 3-8. [2]. Daniel, J. (1995). Dynamic Modeling of A reciprocating Air Compressor for use in Predicting Fault Signatures. Second International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques. Paris. 1-11. [3]. Daniel, J. (1997). Prognostics for A reciprocating Air Compressor. NOISE'CON97.Pennsylvania State University, 195-200. [4]. Fleming, J. (1989). A theoretical and Experimental Investigation of the Flow of Gas Through Reciprocating Compressor Valves, University ofStrathclyde, Glasgow, 117-119. [5]. J.Maclaren. (1975). Vibration and Noise in Pump, Fan, and Compressor Installation, 51-62 [6]. O. Bardou. (1994). Mechanical Systems and Signal Processing. CETIM-Senlis.Fvanc, 551-570. [7]. Shiyuan, L. A D. Ball. F,Gu.(2000). Vibration Monitoring of Diesel Engines Vsing Wavelet Packet Analysis and Image Processing. Research Report, University Manchester. 1-11. [8]. Titze, Ingo, R. (1994). "Principles of Voice Production", Prentice-Hall, Englewood Cliffs, NJ. [9].htt://204.168.68.51/e-tech/tp020/tp020prt.htm
756
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
INERTIAL SENSORS ERROR MODELLING AND DATA CORRECTION FOR THE POSITION MEASUREMENT OF PARALLEL KINEMATICS MACHINES Jian Gao, Phil.Webb and Nabil.Gindy School of Mechanical, Material, Manufacturing Engineering and Management The University of Nottingham University Park, Nottingham, NG7 2RD, UK
ABSTRACT Inertial systems are inherently inaccurate due to a variety of error sources and the interdependent characteristics of inertial variables. These systematic error terms, such as scale factor error and bias uncertainty are usually invariant with respect to time and thus predictable and hence can be compensated by a proper error model. However, an error model cannot completely remove all of the involved errors, the residual errors caused by the unpredictable error component and random error will always be present and restrict the accuracy inertial system performance which cannot be solved by inertial data alone. Therefore, data correction by external measurements is necessary for the inertial system to improve its position accuracy. In this paper an error model to correct the systematic errors, and a data correction method are described which update the random errors in the inertial system. The error model is applied to a simple axis and is used to correct the linear measurement obtained from the accelerometer. The external measurements are obtained through the machine's encoder which are used to update the random errors of the inertial data. The experimental result shows that the position accuracy by the accelerometer was improved by 55% through the error model and external data correction.
KEYWORDS Inertial Sensors, Accelerometers, Error Model, Data correction, Error Compensation, Parallel Kinematic Machine (PKM)
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INTRODUCTION The key to the positional accuracy of a Parallel Kinematic Machine (PKM) is the precision to which the parallel link (or leg) length can be measured. In most parallel kinematic machines, each leg's length is measured by a rotary encoder which is mounted on the leg. However, the method suffers some problems. For example, it cannot measure the deformation of the mechanical structure caused by mechanical effects such as backlash, wear, and thermal expansion caused by the fraction of ballscrew. To compensate for these errors and achieve accurate platform position, elaborate measurements with comparative measuring devices such as laser interferometers are needed, these are very expensive[l]. To greatly reduce this cost, a low-cost solid-state inertial sensor based measurement system is proposed in this paper to directly measure the actual position of the PKM. Inertial sensors have been widely used in aerospace for guidance and navigation applications, but because of the inherent errors within inertial sensors and application environment effects, inertial measurement systems cannot provide high accuracy position information for most positioning systems. These systematic errors are predictable and therefore can be maximally compensated by developing reasonably detailed models of the inertial platform, but random error cannot be corrected in this way. In order to bound random drift error and residual systematic error, external measurements from different sources can be used to periodically correct the inertial data. Under the correction of error modelling techniques and external measurement, the inertial positioning system can provide valuable information for positioning applications.
INERTIAL SENSOR ERROR ANALYSIS All inertial sensors are subject to a variety of errors which limit the accuracy of inertial system. Inertial sensor errors are generally due to mechanical imperfections in the sensors and electrical imperfections in the associated instrumentation. The dominant sources of error present in accelerometers are listed as follows [2]: a) Measurement noise: random error added to the measurement. It includes electrical noise and environmental noise which depends on vibration amplitude and could have some frequency components higher than others. b) Bias error: is a bias or displacement from zero on the measurement when the applied acceleration is zero. The size of the bias is independent of any motion to which the sensor may be subjected. c) Scale factor error: errors in the ratio of a change in the output signal to a change in the input which is to be measured. Scale factor errors may be expressed as percentages of the measured full scale quantity or simply as a ratio; d) Scale non-linearity: deviation from the desired linear input/output relationship. e) Cross-axis coupling error: which give rise to a measurement bias under certain conditions when the sensor is subject to vibration along the sensitive and pendulum axes simultaneously. Cross coupling is often expressed as a percentage of the applied acceleration. f) Accelerometer minimum threshold: a lower limit below which small changes in input cannot be detected by sensors. Threshold can be regarded as a dead band around null which effects the output of small acceleration or angular rate input. Bias Error Among the error items described above, biases of inertial sensors are the main sources which cause drifts in the velocity, position and attitude information. These drifts are determined from inertial sensors through a transformation and integration process and such integration processes result in unbounded growth in the position and velocity errors.
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When no acceleration is applied to an accelerometer, the output voltage of the accelerometer is referred to as the zero-g bias level. The bias error will shift the sample mean value away from the true mean value of the measured variable by a fixed amount. Accelerometer bias accumulates into displacement error according to the following equation
where b^ is the bias acceleration, Sd is the displacement error through the travelling time /. The kinematic equation shows that an uncompensated accelerometer bias builds up displacement error as the time's square. For the application of PKM positioning system, an uncompensated accelerometer bias error of Img in a 100mm movement lasting 2 second (supposing the initial velocity is zero) gives a distance error 20mm. Furthermore, zero-g bias of an accelerometer is highly temperature sensitive. It may also slowly change over time, perhaps from the ageing of internal components. As variations occur in the ambient temperature, zero bias will exhibit some temperature drift. When the accelerometer used to measure low g levels over wide temperature ranges, the zero-g drift can become large in proportion to the signal amplitude. On the other hand, a gyro bias will cause the angle error by integrating over time which works as a time-varying misalignment and cross-couples into the accelerometer in 3-axes inertial system causing an error in position. In this paper, the determination of PKM position (not contain orientation) is the main focus, and gyro bias will not be described here. Scale factor error The scale factor is the ratio between a change in the output signal and the change in input. Scale factor drift defines the amount by which an accelerometer's sensitivity of measurement varies as ambient condition change. These conditions could be the temperature or frequency of the measured motion. Thermal effects on bias and scale factor errors can be very significant and are often difficult to model accurately. This is because in some sensors, temperature gradients within the sensors can alter the performance of many of its components. As variations occur in the ambient temperature as well as the internal components, the scale factor will change the value by a function of temperature. Considering the accelerometer in this work, the sensitivity is lOOOmV/g under the standard ambient conditions of temperature, frequency, etc. With the calibrated information specified by the sensor supplier, the scale factor drift with temperature is specified as K^,^ = 0.02%F5'/°C (reference temperature is 22°C). So the corresponding scale factor drift e^p under the measure temperature (T°C) can be calculated by the expression of e^^ = AT,, • (r°C - 22°C). Cross-coupling errors Since an accelerometer is a directional device, ideally, it should only be sensitive to its axial motion. However, the actual sensing direction of the accelerometer is not just the direction along its primary sensing axis, ft also senses the motion in a plane that is perpendicular to the primary sensing axis. This sensitivity to orthogonal acceleration components is called cross-axis or transverse sensitivity which primarily arises as a result of manufacturing imperfections even in well designed transducers. Crosscoupling or cross-axis sensitivity is often expressed as a percentage of the applied acceleration and is generally less than 5 percent. But for highly demanding applications, the error caused by the cross-axis sensitivity will seriously contaminate the experimental data [3].
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SYSTEM ERROR ANALYSIS Besides these errors of inertial sensor, some other errors occur in inertial systems such as initial misahgnment and numerical integration process. In addition, it should be noted that the dynamic environment affects the way in which these error sources propagate into inertial system errors. Therefore, vibration error analysis for the application is performed because of the hostile environment and severe vibration experienced in machine tool applications. Alignment Errors Inertial sensors attached to the platform will resolve their measurements relative to inertial space along the sensitive axes of the instruments. In most systems, the instrument-sensitive axes are nominally aligned with the platform axes. Perfect alignment with their assumed directions is not possible under realistic conditions. To visualise the problem, assume that an accelerometer was mounted on a platform to measure its horizontal motion. In theory, the accelerometer shouldn't measure any acceleration along its sensing axis in a steady state. However, the platform may tilt a small angle along the axis under realistic condition, so the accelerometer will sense an acceleration component of gravity. Since the tilt won't disappear during its movement, the gravity component contained in the output of the accelerometer will be treated as a part of acceleration, which will build up the position error (actual as time t^. Assume the misalignment angle along sensitive the sensitive axis is p shown in figure 1, then the axis) gravity component in accelerometer data at the initial position can be expressed as
Figure 1: Gravity component contained in acceleration data due to initial alignment error
(2)
gsinfi
Therefore, the velocity and position error due to the misalignment are derived by time integral: V^=g-smj3't
(3)
P.=\^/=\g'^^^J3'^'
(4)
Integration Errors According to the error analysis, the output of the inertial sensors in one direction can be written as (5) 0)^ = co-i. -\r5co- 0), + 5(0^ + w
(6)
where, a^.co^ are the measured acceleration and angular rate, and the a-i^co, are the true acceleration and true angular rate that should be measured by the accelerometer and gyroscopes. Sa^,5co^ represent the other errors accept the random.
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The incremental velocity and position is then obtained by integrating the Equations (5) and (6): F = Fy. + Saj + \wdt P = Pj.+-Saf + \jwdt 2 And e = O.J. + Scoj -f jwdt
(7) W (^)
In these equations from (5) to (9), the terms of Sa^, Sco^, cause errors in velocity and attitude growing linearly with time, while the error in position grows quadratically with time. Therefore, integration will introduce important errors to the derived inertial parameters because of the noise or errors contaminated in the measured data. This mainly causes an offset in the derived velocity and drifting error in the double integrated position. Vibration Dependent Errors The inaccurate characteristics of inertial systems is mainly due to the above discussed errors, which occur in every inertial system. However, the dynamic environment of the inertial application also affects the result of system accuracy. For the machine tool application, the platform vibration is a severe source of errors for the inertial system. Owing to limited sensor bandwidth, dynamic mismatch between sensors and insufficient computational speed, which prevent the system from interpreting such motion correctly, vibration or oscillatory motion can cause inertial system errors. The effects of sculling known as the combination of angular and translational motion can be particularly detrimental to system performance. In the presence of such motion, if the inertial system fails to detect the motion and accurately process the inertial measurements, then significant system errors can arise. The sculling errors [4] will cause an acceleration bias through failure to take account of the rapid changes of attitude occuring between successive acceleration resolutions.
SYSTEMATIC ERROR CORRECTION Because of errors (systematic errors and random errors) contained in a measurement system, the accuracy of the measurement can never be certain. The importance of a measurement error is that it obscures the ability to ascertain the desired information: the true value of the variable measured. Therefore, an error model is required to correct the effects of the predictable systematic errors on the accuracy of inertial sensors (random errors cannot be compensated by this way). In the error model, the predictable error components and their coefficient can be represented by an equation and hence modelled mathematically. These predictable error components can be estimated from observations of performance and used in the opposite sense to correct or compensate for the imperfections in the sensor performance. Based on the error analysis described in previous sections, the output of an accelerometer along the x axis may be expressed in terms of an applied acceleration and sensor error coefficients and system error coefficients as follows:
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1
(11)
where ^^.^ represents the true acceleration applied in the direction of the sensing axis, which should be measured by the accelerometer; a,„^ represents the measured acceleration, K^j is the scale factor of the accelerometer, a^ and a, are the accelerations applied perpendicular to the sensitive axis x, 5^., and S.^ represents the cros-coupling factors, b^ is zero bias error (or offset error), e^P is scale factor error caused by temperature's change, e^^ represents the misalignment error in initial position, w represents random noise. Generally, these coefficients of zero-g bias, cross-coupling and scale factor error can be measured and correction can be therefore applied to compensate the repeatable components of these errors. However, some errors are not constant all the time, such as the biases are temperature dependent errors and have switch-on to switch-on variations. Scale factor errors are also temperature dependent errors. The random bias and random scale factor error are always present, random noise and vibration are also variant with the dynamic environment. These errors are less predictable and therefore cannot be easily compensated.
RANDOM ERROR CORRECTION Once the acceleration data was corrected through the error model, the observable systematic errors are compensated, but as described above, it still contains random errors, such as white noise, offset drift, random scale factor errors and random transverse sensitivity. These errors will build up with time, and cause drifting errors in the derived velocity data and final position. Because these errors are timedependent and the inertial variables are inter-relative, it is impossible to correct the errors by inertial sensors alone, external measurements must be used to update periodically the inertial data. In this paper, the exact reference data provided by the motor encoder are used to correct the accelerometer data in which the systematic errors has been partly removed through the error model. Based on the equation of (11), there is
where QJ^ (/) represents the true acceleration that the accelerometer should measure, a^^(r) is the measured acceleration after removing the bias error, C| is the system scale factor, CQ is a constant which combines the rest of the systematic errors and random en*or. The equation (12) can be restated by equation (13)
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a„,{t) = —a,{t) + -^ = K,a^,{t)^K, C,
(13)
C,
Suppose the initial velocity and initial position are zero, then the measured displacement can be derived by double integration from above equation as: '2
>
Xt^
K
y{t) = K,x \dt, \dt, xa.,. (t) + -^—0
0
(14)
^
The true (error free) displacement x(t) is expressed as I
'2
x(t)= Idt^ldt^xa^j-iO 0
(15)
0
When the error free displacement x, ,^2 are available at Tj ,^2, then
yi =K,xx,+
^ ' ,y^ =K,xx^+
-^ -
(16)
Thus the two coefficients K^, Kj can be obtained by solving this system linear equations 2{y^xx^-y^xx,) ^1 =
~TZ
y2'^tl ~ ;T~'^2 =
-fZ
y.xt;) 'pT
(17)
Therefore the corrected displacement can be obtained by the following expression: .^ 1 . .X 40 = —x(y(0
^2X^\
'-y-)
(18)
The performance of the error model and data correction method introduced above was validated through experimental data. The experiment was carried on a PKM leg testbed, a capacitive accelerometer was mounted on the leg to measure the acceleration of the linear movement and a laser interferometer was used to measure the real displacement as a standard reference. Figure2 shows the RMS values of the displacement results from raw data, error model modified data and external data corrected data. Due to the noises contaminated in the measured data, the RMS of the displacement is 13.mm for 100mm linear movement. But the error can be reduced about 18% through the error model compensation. And further error reduction of 37% can be achieved by the external two points correction.
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lOOnmlioear Movem^
RawData
ErrorModel
KKcorrect
Figure2. Performance of the error model and the data correction.
CONCLUSION Systematic errors and random errors contaminated in inertial system restrict the achievable accuracy. Systematic errors may be compensated by the application of error modelling technique which is dependent on precisely how the coefficients in the error model represent the actual sensor errors. On the other hand, it can be noted that the random errors also cause severely drifting error in velocity and position, especially for the long-term tracking applications. In order to curb error growth, an external reference system has to be used. If two exact reference points are available, then the correction can be made by equation (18) to update the measurements of accelerometer. Therefore, with the error model and data correction by external measurement, coupled with the proper signal processing, both systematic error and random error in inertial system can be corrected.
REFERENCES 1. Whittingham, B. Capabilities of Parallel Link Machine Tools: Preliminary Investigations of the Variax Hexacenterd. in ASME International Mechanical Engineering Congress and Exposition. 1998. Anaheim, California, USA,. Lawrence, A., Modern Inertial Technology: Navigation, Guidance, and Control. 1998, New York: Springer. McConnell, K.G., Vibration Testing: Theory and Practice. 1995: John Wiley & Sons, Inc. Titterton, D.H. and J.L. Weston, Strapdown inertial navigation technology, ed. P.B. E.D.R.Shearman. 1997: Peter Peregrinus Ltd.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ON-LINE SENSOR CALIBRATION VERIFICATION "A SURVEY" J. W. Hines, A. Gribok, and B. Rasmussen The Maintenance and Reliability Center The University of Tennessee Knoxville, Tennessee 37996-2300
ABSTRACT On-line, real-time, sensor calibration verification techniques have been under study for almost two decades. Several techniques have been developed that use significantly different data-based modeling algorithms. Three of these techniques: the Multiple State Estimation Technique originally developed by Argonne National Laboratory, the Autoassociative Neural Network technique currently employed by Halden Reactor Project's PEANO system, and the Non-Linear Partial Least Squares technique currently used by The University of Tennessee's researchers, have progressed to become viable options for sensor calibration monitoring. These three techniques are compared on the basis of several performance considerations including development effort, scalability, consistency, non-linear modeling capabilities, and the ability for the system to adapt to new operating conditions. In addition to these performance attributes, we also compare their availability on the commercial market and their experience base.
KEY WORDS Sensor Calibration, Neural Networks, MSET, Inferential Sensing, Non-linear Modeling, Fault Detection and Isolation.
INTRODUCTION As companies move towards condition-based maintenance philosophies, new technologies are being developed to ascertain the condition of plant equipment. This paper looks at three methods used to monitor the condition of sensors and their associated instrument chains. Currently, the most common method used to assure sensors are operating correctly is periodic manual calibrations. This technique is not optimal in that sensor conditions are only checked periodically; therefore, faulty sensors can continue to
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operate for periods up to the calibration frequency. Operating a plant with faulty sensors can cause poor product quality, poor economic performance, and unsafe conditions. Periodic techniques also cause the unnecessary calibration of instruments that are not faulted which can result in damaged equipment, plant downtime, and improper calibration under non-service conditions. It is obvious that a technology that can accurately predict the condition of an instrument during operation can increase product quality, plant efficiency, safety, and profitability. University of Tennessee (UT) researchers have been pioneers in the development of online sensor calibration verification systems. Dr. Belle Upadhyaya was one of the original investigators in the 1980's [Upadhyaya 1895, 1989], through a Department of Energy funded research project to investigate the application of artificial intelligence techniques to nuclear power plants. Researchers at Argonne National Laboratory continued with similar research throughout the late 1980's and 1990's [Mott 1987] in which they developed the Multivariable State Estimation System (MSET) which has gained wide interest in the US Nuclear Industry. Chicago based SmartSignal Inc. licensed the MSET technology for application to other industries [Wegerich 2001]. Several other US companies such as Pavillion Technologies, ASPEN IQ, and Performance Consulting Services [Griebenow 1995] have also developed sensor validation products. The major European player in this area is the Halden Research Project where Dr. Paolo Fantoni and his multi-national research team have developed a system termed Plant Evaluation and Analysis by Neural Operators (PEANO) [Fantoni 1998] and applied it to the monitoring of nuclear power plant sensors. Many other researchers have been involved with inferential sensing on a limited scale and also with the verification of redundant sensors.; these techniques are not evaluated in this paper. The objective of this paper is to review techniques for plant wide sensor monitoring. Also note that the techniques surveyed do not include analytical redundancy based fault detection and isolation (FDI) techniques that are based on physical models. Several articles surveying FDI techniques already exist [Isermann 1984, Frank 1987, Gertler 1988]. Numerous data-based technologies have been used by major researchers in the field including autoassociative neural networks [Fantoni 1998, Hines 1998, Upadhyaya 1992], fuzzy logic [Hines 1997], principal component analysis [Qin 1999], non-linear partial least squares [Qin 1992, Rasmussen 2000a], and kernel based techniques such as MSET [Singer 1997] and the Advanced Calibration Monitor (ACM) [Hansen 1994]. This paper will survey three technologies used in the Nuclear Power Industry that use different databased prediction methods: a kernel based method (MSET), a neural network based method (PEANO), and a transformation method (NLPLS). First, a brief review of the theory will be given, then the methods will be compared using several performance indicators; and finally, example applications will be presented.
METHODOLOGIES This section will present concise descriptions of the three prediction methodologies surveyed in this paper. For a more complete description of the theory, additional
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references are given. All of these techniques use historical plant data that is assumed to be error free and cover the operating space of the system or process. From this data, predictive models are constructed which are used to predict sensor values for previously unseen data. Figure 1 is a block diagram of a basic sensor calibration monitoring system in which a vector of sensor measurements (x) are input to a prediction model which calculates the best estimates of the sensors (x'). The estimates are compared to the measured values forming differences called residuals (r). A decision logic module determines if the residuals are statistically different from zero and establishes the health or status (s) of each sensor. Measured Signal Values ues ^1 X'^i
,. "
Signal Predictions
^ Prediction Model 1 Z
Prediction Residuals
Sensor Status
1*
x' Comparison Module
s Decision Logic
J Figure 1. Sensor Calibration Monitoring System Diagram
There are several methods used to perform the decision logic including limit checking, statistical process control charts, and the sequential probability ratio test (SPRT) originally developed by Wald [1945]. These methods are not the focus of this survey, but can be investigated further in papers by Gross [1992] of Argonne or Yu [2001] from the University of Cincinnati who is currently working with Argonne. Multivariate State Estimation Technique (MSET) Non-parametric regression methods such as kemel regression [Cherkassky, 1998] or MSET, which proves to be a kemel regression method in disguise [Zavaljevski, 1999], have been used for sensor calibration verification. MSET is a non-linear kemel regression technique that utilizes a similarity operator to compare a set of new measurements to a set of prototypical measurements or states [Gross et al. 1998]. This comparison process generates a weight vector that is used to calculate a weighted sum of the prototype vectors to provide an estimate of the true process values. The similarity function uses one of two proprietary similarity/distance operators [Singer 1996]. MSET functions as an autoassociative model, reproducing an estimate of each of a set of measured signals that are provided as inputs to the model. The presented derivation of the MSET algorithm comes from Black [1998] but originated in Singer [1996]. Single underlined symbols represent vectors while double underlines represent matrices.
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MSET is similar in many regards to multiple linear regression. In linear regression, we let A, referred to as the prototype matrix, represent a matrix assembled from selected column-wise measurement vectors, and let w represent a vector of weights for averaging A to provide the estimated state ]C_ as follows: x^=Aw
(1)
The column-wise measurement vectors which make up the prototype matrix 4 ^re selected by a proprietary technique that is carefully performed to provide a compact, yet representative, subset of a large database of measurements spanning the full dynamic range of the system of interest. If e represents the difference between an observed state x and the estimated state x^, then the following relations may be constructed: e^ = x-x^ = x^-Aw
(2)
The least squares solution to the minimization of e yields the following expression for w, (where the left hand factor of the matrix product is known as the recognition matrix): }V = (A''
'^~'
'{A''
-x)
(3)
A chief liability of this linear method is that linear interrelationships between state vectors in A result in conditioning difficulties associated with the inversion of the recognition matrix. This shortcoming is avoided by the MSET by applying nonlinear operators in lieu of the matrix multiplication. These operators generally result in betterconditioned recognition matrices and more meaningful inverses of the recognition matrices. MSET extends the multiple regression equations to include a non-linear operator as follows:
W^IA" eAy(A'' ®x)
(4)
The ' 0 ' symbol represents an appropriate similarity operator which is also termed a kernel operator in non-parametric regression [Cherkassky 1998]. A typical kernel operator is the gaussian operator: K(u)=(27ia)'^^*exp(-u^/2a^). An estimate of the plant states can then be given as:
x_'=4'"y'" e^j''^"" ©^)
(5)
Note that the estimate is dependent on a matrix of prototype states A, the current state x, and the choice of kernel operator and its spread constant a. The most mathematical intensive operation in this equation is the matrix inversion which can be done off-line and stored since it does not depend on current input values. This allows MSET to operate in real time.
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Non-Linear Partial Least Squares (NLPLS) The Non-Linear Partial Least Squares (NNPLS) based system consists of a set of inferential models that, when combined, form an autoassociative design. Each parameter to be monitored requires a separate inferential model. An inferential model infers a prediction of a specific sensor's measurement based on the values of correlated sensors. The sensor values that are used as inputs to each inferential model include all sensor values except for the specific sensor being modeled. A schematic of a NNPLS inferential model is provided in Figure 2.
Inputs X
t Z^^^'£{Osin +z^{Ocos
(1)
Assuming that the deviation angle of the origin O, at which the tip of the vibration pickup is moving in a j;-direction, is (0, its geometrical relations to ^^^(O, ^^{O* and (t) are obtainedfromthe following equations respectively;
(2)
Substituting Eqa2 into Eqn.l, the signal for the QE direction that will be detected at point E is; PE[t)-{f)sm +ecos )sin {t)
BiO,b) E(e,b)
(3)
From b - g-ecos /sin , the above Eqn.3 develops into; p^{t)=qsm
sin (/)
(4)
Similarly to Eqn.2,fromthe geometrical relation; sin (t}=yu(t)/u
-*-yi/(t) Figure 2: Operating principle of the hand-held type triaxial pickup
(5)
Substituting Eqn.5 into Eqn.4, it is found that the signal detected at point E is proportioned to the horizontal motion, y^jit), of the surface which the pickup is in contact with, and after elimination of any components attributed to the rocking motion, the following equation is obtained; /'£W=>^t/W^/«)sin
(6)
In Eqn.6, the signal /^^(O* which is detected by the sensor positioned at point E, is expressed by a function for the intersection angle , produced by sensitive axis QE and the centerline, and the ratio of the distance OQ to OU. The intersection angle can be structurally determined but the origin O is a pivot for the pickup motion that depends on the vibratingfrequencyof the contact point U. To make the signal p^it) independent of the position of the origin O, the geometrical structure of the pickup must be q=u. That is, when the intersection point Q coincides with the contact point U, the signal detected by the sensor positioned at point E, becomes;
795
P£(0=>'(y(Osin
(7)
The detected signal obtained from Eqn.7 above reveals that it is identical to the signal obtained by a conventional vibration pickup that isrigidlyattached to point U. Detection of Horizontal Vibration and Vertical Vibration The signal detected by a sensor, while the contact point U is moving vertically (in the z direction) at Zu(t), is determined exclusively by the geometrical relation Zu(t)cos . Therefore, when the contact point U, is moving horizontally and vertically, the signal detected by the sensor is expressed as; P£(0=3^o(^)sm -\-z^.{t)cos
(8)
The signal detected by the sensor positioned at point N is identical to the signal detected by the sensor at the point E, as far as vertical motion is concerned, and it shows the opposite phase of horizontal motion. As for the signal Pj^ (t), detected at point N, the sign in the first term of therightside of Eqn.8 isreversedas follows;
pAf)=-yu(fh^
+^o(^)cos
(9)
Eqn.8 and Eqn.9 are for signals which are detected by the two sensors attached to points E and N respectively, while the contact point U is moving, andfromthe sum of the equations or their difference, the motion components in vertical direction and horizontal direction can be obtained. The vertical component is expressed as;
,^.{,)=Mh£M
(10)
2 COS
and the horizontal component is expressed as;
(/)=MizMl 2sm
(11)
Detection of Two Orthogonal Horizontal Vibrations and Vertical Vibration In the above discussion only one of the horizontal dimensions has been discussed but for horizontal motion two orthogonal dimensions must be detected. To detect the two dimensions of horizontal motion, position three sensors horizontally around the z-axis at interval , = 2 /3 as shown in Fig. 3, and simply compensate for the above equation with the geometrical position of sensors according to the method as shown below. Signals /?i(f), P2(t) and p^(t) which are detected by sensors 1, 2 and 3 while the contact point U is moving vertically (in the z-direction) at Zu{t)y and horizontally (in the ydirection) atyu(t), and also horizontally (in the jr-direction) at Xu(t), which is orthogonal to the y direction, are obtained as follows; pM=yu(f)^^ +zt/(0cos P2(t)=-Xij(t)sin sin +yy(t)cos sin +Zij{t)cos Pi(t)=Xij{t)sm sin +yy(t)cos sin +Zu(t)cos
796
(12) (13) (14)
By substituting =2 / 3 , the vibration components Xy(t), yyit), and Zy(t) are obtained from the signals p,(0, JD2(0 and /^jl?) as follows;
M-.PAt)-P2Kt) 2 sin sin
(15)
y^{^y'^PAthPi[t)-pAt) 3 sin
(16)
2^{^)^PM±PJM±PM.
(17)
3 cos Sensor3
z Leaf Spring k Sensorl
J
Sensor!
7
Figure 3: Structural design of the hand-held type triaxial pickup STRUCTURE AND CHARACTERISTICS OF THE HAND-HELD TYPE TRIAXIAL PICKUP We have developed the hand-held type triaxial pickup based on the operating principles described in the previous section and measured its frequency responses and directional characteristics. Figure 3 shows the structural design and Fig. 4 shows the appearance of prototype. Eqn.15, Eqn.l6 and Eqn.l7, indicate that the amplitudes of the three orthogonal components depend on the intersection angle of the sensitive axes. In order to make the output levels identical in each direction, the optimal intersection angle must be /4. However, this value necessitates a larger diameter for the pickup. Since the pickup is used for machinery diagnosis, it is desirable that an operator can grip it Figure 4: Appearance of prototype pickup tightly. We decided the diameter of the pickup to 30mm and the intersection angle to 20 degrees, which is lower than the optimum value. The three piezoelectric accelerometers are pressed into fixing holes, which are prepared at 120 degree intervals around a circle on block in Fig.3. The sensitive axes cross the centerline at the tip of the pickup with 20 degrees respectively. The three combined elements can be regarded as a rigid body within the frequency range up to IkHz. The sensitive axes as pickup itself consist of the z-axis, which extends along with the centerline, and the >^-axis, a direction of one of the sensitive elements in a plane orthogonal to the z-axis, and the x-axis, which is orthogonal to the z-axis and the j^-axis.
797
»
1«
-2«
fc
2k
iK
«i
Figure 6: Directional characteristics of the Y-axis
Figure 5: Frequency responses
Figure 5 shows the frequency responses of the prototype pickup in the z-axis and the ^^-axis. Both the z-axis and >'-axis are ahnost flat up to IkHz. The contact resonance determines the maximum frequency of the pickup. Generally it is desirable that the contact resonance is three times higher than the maximum frequency. In conventional triaxial vibration pickups, the horizontal contact resonance frequency is one third of the vertical contact resonance frequency. The contact resonance frequency depends on the pickup mass and the stiffness at the contact surface. From the difference shown in the contact resonance frequencies it is expected that the stiffness in the shearing direction is lower than that in compression direction. For the pickup we have developed however, the contact resonance frequencies of the horizontal x- andjv-axes are 5.2kHz respectively, and the vertical z-axis is 3.5kHz. That is, the horizontal direction is higher than the vertical direction. This is because the stiffness at the contact surface is the same but the dynamic effective mass of the pickup is smaller. The dynamic effective mass is a quantity that is proportional to the force, which is required to make motion. The center of gravity of the present pickup is located away from its tip, which allows it to cause a motion in the horizontal direction with a smaller force. The horizontal effective mass is smaller than the actual mass, but in the vertical direction the actual mass becomes the dynamic effective mass. Figure 6 shows the directional characteristics of the ^-axis when the prototype pickup is pressed onto a surface vibrating horizontally at 160Hz, and then rotated around the z-axis. In a plane orthogonal to the z-axis, the characteristics coincide with a cosine curve in a range of ±70 degrees from the >'-axis, with tolerance ±5%. The lateral sensitivity at 90 degree is 8 %. It is not as good as conventional unidirectional pickup whose lateral sensitivity is 5%, but it is not a significant problem for practical
ORBIT VISUALIZING VIBROMETER We have developed a vibrometer, which is equipped with a hand-held type triaxial pickup whose frequency response is almost flat from lOHz to IkHz and the full-scale range is lOOnmi/s.
798
Figure 7: Orbit visualizing vibrometer
Figure 7 shows the appearance of the vibrometer. We adopted a 128x64 dots LCD which displays measurement data. The display can be switched between numerical value mode and orbit mode. Figure 8 shows an example of numerical value mode. This mode displays four items, vibration velocities in three directions and a composite value of them. The composite value is a scalar value that does not depend on the attached direction of the vibration pickup. In orbit mode the vibrometer samples 1024 points of waveform data of velocity with 2,56kHz sampling rate in three directions and displays them after converting to displacement. Figure 9 displays an example of an unbalanced state. The unbalance is caused by centrifugal force with an uneven rotating mass and it shows an orbit of close to circularity in a plane orthogonal to the rotation axis. Figures 10 and 11 show examples of misalignment. These orbits indicate parallel misalignment where the shafts are coupled by a miniature flexible coupling. The both hubs of the coupling have two connection arms as shown Fig. 12. Figure 10 is the orbit on a plane orthogonal to the rotation axis and Fig. 11 is the orbit on a horizontal surface. You will find in Fig. 10 that the axis moves to the same position after rotating four times. This is caused by the 4th order harmonics. Thus the present vibrometer allows us to visualize the movement of attached point and provides information that is very useful for machinery diagnosis. The present vibrometer can store 100 sets of numerical value mode and 8 sets of orbit mode. In the orbit mode one data set includes vibration waveforms in three directions and their reference signal, for which a waveform or rotating pulse signal is used. The stored vibration waveforms can be transferred to personal computers and used in animated displays of multiple points.
Measure Ux 3.6mm/sl U^ 14.9mm/sH Uz 31.6mm/sl Us 35. Imm/'sl Figure 8: Example of numerical value mode
Figure 9: Orbit of unbalanced state 1 -irwa ORBIT
Y-Z DISP
^'^'^^
^1^ a\
...L...11..
Oc
•f""/V
^
lOtxin
Figure 10: Orbit of misalignment orthogonal to the rotation axis
Figurell: Orbit of misalignment on horizontal surface
Conventionally, most diagnosis of fault of structure is carried out based on frequency data, but by using the orbit vibrometer further detailed information on time data as well as phase data can be obtained that will significantly improve the accuracy of diagnosis. Figure 12: The coupling we examined
799
\
CONCLUSIONS Herein we have presented and discussed a pickup based on a new principle that allows the three orthogonal vibration measurements simultaneously by pressing it onto a machine surface. The purpose of the pickiq) is to perfomi accurate diagnosis of fault of structure. For that purpose, the pickup is designed for use up to 1 kHz, which is defined as the vibration severity. We have made a prototype pickup and demonstrated its characteristics. It has been proved that the prototype has sufficient perfomiance for practical use. We have also made the orbit visualizing vibrometer, which is provided with the pickiq). It displays the orbit of the measured point on its screen. Using the present vibrometer, we introduced actual examples of fault of structure and showed that the displayed orbits could provide very useful information for diagnosis. Using personal computers and the vibrometer that is able to store waveforms, any phenomenon that results from combined waveforms at multiple points can be displayed, and allows further visualization of the movement of a structure as a whole. We believe the field of multi-dimensional diagnosis technology will be established in the near future and the present technology will contribute to diagnosing rotating machinery.
REFERENCES [1] Yokota A., Komura H., and Tokita Y. (1993). Study on the Hand-held Type 3D Vibration Pickiq). Proc. Spring Meet. Acoustical Society of Japan, Vol.1,421-422 [2] Yokota A., Komura H., and Tokita Y. (1994). Study on the Hand-held Type 3D Vibration Pickiq) Part II - Estimation of effective mass of horizontal axis direction. Proc. Autumn Meet. Acoustical Society of J^an, Vol.1, 547-548 [3] Yokota A., Komura H., and Tokita Y. (1996). Development of Hand-held Type 3D Vibration Pickup. The Journal of the Acoustical Society of J£q)an 52:
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DEVELOPMENT OF AN ON-LINE REACTOR INTERNALS VIBRATION MONITORING SYSTEM(RIDS) J.-H. Park', J.-B. Park^ C.-H. Hwang\ E.-S. Choi^^ ' Department of Advanced Reactor Technology, Korea Atomic Energy Research Institute, 150 Duck-Jin Dong, Taejon, Korea ^ Korea Electric Power Research Institute, Mun-Ji Dong, Taejon Korea ^ Giga Communications & Energy Inc., Gal-Ma Dong, Taejon, Korea
ABSTRACT An on-line reactor internals vibration monitoring system called by RlDS(Reacor Internals Diagnostic System) has been developed for monitoring the vibrational modes of a core support barrel in a nuclear reactor under full power operation. The core support barrel is a key structure which supports and protects nuclear fuel assemblies and suffers flow induced vibration due to the high pressure primary coolant. This system acquires the noise signals from the ex-core neutron detectors and performs real time basis time and spectral analyses to diagnose the natural vibration modes of the core support barrel using the real time mode separation algorithm. It is comprised of signal isolators & signal conditioners, digital signal processor module, and a host computer system with PCI interface. Also the application software has been developed to implement data storage, time and spectral domain signal analyses and improve diagnostic functions compared to the conventional reactor internals vibration monitoring systems. This system will be installed in Korean nuclear power plants in which it has not been equipped yet, for the early detection of potential problems related to the structural integrity of the reactor internals. The neutron noise signal analyses using RIDS have been implemented for the Yonggwang unit 1 to build baseline data. It reveals that the 1^^ beam mode and the 1^^ shell mode natural frequencies of the core support barrel are about 7.5 Hz and 21 Hz, respectively.
KEYWORDS Vibration, Monitoring, Reactor Internals, Core Support Barrel, Digital Signal Processor, Neutron Noise, Mode Separation, Beam Mode, Shell Mode.
801
INTRODUCTION The reactor internal structures which consist of many complex components are subjected to flowinduced vibration due to the high pressure reactor coolant flow. The flow-induced vibration may cause degradation of the structural integrity of the reactor internals such as degradation or failure of the preload condition of the hold down ring in the upper part of the reactor vessel, and result in loosing the mechanical binding components which might impact other equipment and component or cause flow blockage. The above phenomena may cause significant core damage and cannot be detected in advance by conventional detection methods and even detected, after serious damage occurred on these components. In order to prevent this kind of problem the integrated on-line monitoring system for the reactor intemals(called by IVMS; Internal Vibration Monitoring System) has been developed by using the reactor noise techniques(ABB-CE, 1994; Y.-S. Joo et al, 1995; J.-H. Park et al, 1999). The reactor noise is defined as the fluctuations of measured instrumentation signals during plant fullpower operation, which have information on the reactor system dynamics such as neutron kinetics, thermal-hydraulics, and structural dynamics. These noise signals can be obtamed directly from the existing instrumentations of the reactor system without any effect on the reactor operation. It is well known that the structural integrity of the reactor internals can be monitored and diagnosed by using the structural dynamic components of the ex-core neutron noise measured by the neutron detectors located around the periphery of the reactor pressure vessel(J.A. Thie, 1979; EPRl, 1987; ASME/ANSl 0Mb, 1989; J.-H. Park etal, 1999). In Korea, there are still several nuclear power plants in which the IVMS has not been equipped yet. Thus a state-of-the-art reactor internals vibration monitoring system(RIDS; Reactor Internals Diagnostic System) has been developed for installing to those plants which have been operated for over 10 years.
SYSTEM DESCRIPTION RIDS system has been developed to comply with ASME/ANSI 0Mb Part 5, "Inservice Monitoring of Core Support Barrel Axial Preload in Pressurized Water Reactors". The system configuration is illustrated in Fig. 1. This system has various kinds of high performance capabilities such as simultaneous signal isolation and conditioning, 8 channel real time FFT(Fast Fourier Transform) with a maximum sampling frequency range of 100 kHz per channel, and a real time display of each natural mode power spectra, etc. And it can easily be expanded to incorporate other additional functions; for example, fuel assembly vibration and loose part signal monitoring. Hardware The schematic of the system hardware is shown in Fig. 2. The hardware system consists of Signal Isolators, Signal Conditioners(one per each input channel), DSP module, and a Host Computer. The Signal Isolator is provided for protecting the plant instrumentation from being affected by the unexpected connection of the input signals. Each Signal Conditioner has a programmable signal amplifier and an anti-aliasing filter. Thus the incoming raw neutron signals through the Signal Isolators can be filtered and amplified automatically through the application software setup options. The DSP module has 2 DSP boards, 8 A/D converters(16 bit, 4 per each DSP), 2 digital signal
802
processor chip(TMS320C31), and its own memory(4 Mbytes/board). The data digitized from A/D converters are processed for computing user-defined functions in the DSP chip, and then the results are stored in DSP memory on a real time basis. The stored data are again transferred into the main computer memory by way of PCI interface for utilizing in the application software. The Host computer is a type of industrial PC(Pentium III, Intel 500 MHz, Windows NT) which meets the specifications of US military standards against high temperature and shock vibration. It is equipped with several PCI interface slots for communication with various kinds of signal processing units, and for expanding further DSP boards. Also a high capacity hard disk driver and printing device are attached for analyses data storage and hard copy outputs. Software The system software consists of a DSP driver, a PCI interface program, and a host software as shown in Fig. 3. The DSP driver has two major function programs. One is the main routine which initiates and tests the DSP boards, the other subroutine controls the A/D conversion process directed by the main routine, executes basic function processing including the real time FFT, and transfers the results into the host computer's memory through the PCI interface. The PCI interface program is a kind of utility program that makes it possible to communicate between Windows operating system(Win32 device driver and PCI API DLL) and PCI 1/0 accelerator of the DSP board as shown in Fig. 3. A variety of time and spectral domain signal analyses are implemented in the host software. The digitized time domain data transferred from the DSP board will be used for the real time display of the input signals and computation of the normalized RMS(Root Mean Square), normalized auto and cross PSD(Power Spectral Density), mode separated PSD, and coherence functions, etc(J.-H. park et al, 1999). Microsoft C++ software has been used for the development of the signal analyses and user interface programs in a Windows NT environment. The major functions including database menu are depicted in Fig. 4. The RIDS software also performs system calibration automatically before system operation.
SIGNAL ANALYSIS FOR MONITORING REACTOR INTERNALS A series of ex-core neutron signal analyses using RIDS have been implemented for Yonggwang unit 1 nuclear power plant in order to build baseline data. The real time displays obtained from the Real-Time Data menu are illustrated in Fig. 5 and Fig. 6. Fig. 5 shows the acquiring time signals of the total ex-core neutron detectors of Yonggwang unit 1 and Fig. 6 displays the mode separated PSDs of the core support barrel assembly in real time. The normalized auto PSD(NAPSD)s and the corresponding mode separated PSD(MPSD)s using the lower plane excore neutron signals are summarized in Fig. 7 and Fig. 8. It reveals that the beam mode frequency of the core barrel assembly is 7.5 Hz and that of the shell mode 21 Hz. The detailed technical informations and the related analyses results are described in the reference(KEPRI, 2001). In case abnormal condition occurred, the status informations screen for the whole channels and the
803
related vibration modes are displayed as shown in Fig. 9.
CONCLUSION An On-line Reactor Internals Vibration Monitoring System called as RlDS(Reactor Internals Diagnostic System) has been developed to monitor and diagnose the degradation of axial preload of core support barrel and the vibration modes of the structure by using ex-core neutron noise signals. It provides state-of-the-art tools for monitoring and diagnosing the integrity of the reactor intemal structures on a real time and operator-friendly basis. The neutron noise signal analyses using RIDS have been implemented for the Yonggwang unit 1 to build baseline data. It reveals that the 1^^ beam mode and the P^ shell mode natural frequencies of the core support barrel is about 7.5 Hz and 21 Hz, respectively. This system will be installed in Korean nuclear power plants in which it has not been equipped yet, for the early detection of potential problems related to the structural integrity of the reactor internals.
ACKNOWLEDGMENTS The support of Korea Ministry of Science and Technology is greatly appreciated.
REFERENCES ABB-CE, NIMS-Intemals Vibration Monitoring System, 1994. ANSI/ASME 0Mb PartS, Inservice Monitoring of Core Support Barrel Axial Preload in Pressurized Water Reactors, 1989 EPRl NP-4970, Utility Guidelines for Reactor Noise Analysis, 1987. Jinho Park et al,"Development of Fault Diagnostic Technique using Reactor Noise Analysis," KAERl research report, KAERI/RR-1908/98, 1999. Jinho Park et al,"Dynamic Characteristics of Yonggwang 3&4 Reactor Internals," Proceedings of the Korean Nuclear Society Autumn Annual Meeting, Oct., 1999. KEPRI report, "A Development of Diagnostic System for Reactor Intemal Structure using Neutron Noise," KEPRI/99-C16, 2001. Y.S. Joo et al, "Development of Reactor Internals Vibration Monitoring ystem(RIVMOS) using Excore Neutron Noise," SMORN VII, Avignon, 1995. T.R. Kim et al,"A Study on the Fault Diagnostic Techniques for Reactor Intehial Structures using Neutron Noise Analysis, KAERI/RR/1386/94, 1994.
804
Figure 1: RIDS
Figure 2: Hardware configuration
Figure 3: Software Structure
805
LR!5ssw3yC]r«i^«e^ HO*M
['n»i*jj>«twtfp»tt-
't^ V; T' /
(i5o-Pi)= 2.5-2.0 = 0.5; 8i' = (2,500,000)2"= 6.25x10^2. Q^" = (2,OOO,000)"= 5.657xlO*^ 1 6^1 r
Oi
r . \=
B
-;
E
[41
f ^1 (2,000,000f^rfl-||]
6,25 X 10^2 X -
'
^
4.0x10^^x4.5908
vri;
= 0.3404; E
f . \ Bo ^
"=0.0;
4.0x1012x4 .5908
1 ^
5.657x10^^x0.0 = 0.0
^' ^ (2,ooo,ooo)2-^r[i-—I fn+l
E[ln(t)]=ln(e) - i X I X j;[ln(Ui)e-^i x(l,2or4)
g = 0.116;
i = — = 0 . 4 ; ln(e) = ln(2,000,000) = 14.509; ^ x ^ = 0 . 4 x - ^ ^ =0.0155 p 2.5 P 3 3 ^ft^
Setting n = 20 with U = -
Z'2 000 000^
=
'-
; t =feiluretimesfromthe range (200,000 cycles > t
> 6,000,000 cycles) ; we will have r
N2.5
21 E[ln(t)] = 14.509-0.0155 x X In 2,000,000 ^
i=l
V
1
exp
f ^2.5 ' 2,000,000
((1,2 or 4) =15.3825
J
TheE(w), will be E(W)=-7.0312+ 0.5x15.3825-0.3404+ 0.0 =0.3197 The expected value of the variate W*n, E(W*n), will be given by E(W*)=
P(e,p)lnA + [l-P(e,p)]kiB;
i-y^-iJi-o.io ln(B) = hN^Jl = In 0.05 a
ln(A) = ^^[^^
= I n f ^ ^ ^ l =-2.2513
; E ( W : ) . -0.01x2.2513 + 0.99x2.8904 =2.8390 = 2.8904:
814
Finally,
E(n) . P(e>P)ln A . [ l - P ( e , p ) ] l n B ^ 2J390 ^ ^ ^^^^ ^ ^ Items. ^^ E(W) 0.3197
So, we could make a decision about accepting or rejecting the null hypothesis HQ after the analysis of observation number 9.
A PROCEDURE FOR EARLY TRUNCATION According to Kapur & Lamberson (1977), when the truncation point is reached, a line partitioning the sequential graph can be drawn as shown in Figure 2 below. This line is drawn through the origin of the graph parallel to the accept and reject lines. The decision to accept or reject HQ simply depends on which side of the line the final outcome lies. Obviously this procedure changes the levels of a and y of the original test; however, the change is slight if the truncation point is not too small. As we can see in Figure 2, the null hypothesis HQ should be accepted since the final observation (observation number 5) lies on the side of the line related to the acceptance of ^oI
NUMBER OF ITEMS TESTED
|
Figure 2. A truncation procedure for sequential testing CONCLUSIONS The major advantage of a sequential life testing approach in relation to the fixed size approach is to keep the samples size small, with a resulting savings in cost. It happens that even with the use of a sequential life testing approach, sometimes the number of items necessary to reach a decision about accepting or rejecting a null hypothesis is quite large (De Souza 2000). So, the test must be truncated after a fixed time or number of observations. The truncation mechanism for a sequential life testing approach developed in this work provide rules for working with the null hypothesis HQ in situations where the underlying sampling distribution is the two-parameter Inverse Weibull model. To calculate the expected sample size E(n) of our sequential life testing, some numerical integration procedure (Simpson's 1/3 rule in this work) had to be used. Appendixes 1 and 2 give the development of the Eqn.s necessary to calculate the E(n). In a previous work, using an example similar to the one presented in this paper, and with a non-truncated sequential life test approach for a Weibull sampling model, De Souza (De Souza 2000) was not able to reach a decision about accepting or rejecting a null hypothesis HQ even after obtaining 20 observations. With the truncation mechanism developed in this article, the decision of accepting the null hypothesis was reached with the analysis of only 9 observations or items. This fact shows the advantage of such a truncation mechanism to be used in a sequential life test approach.
815
REFERENCES De Souza, Daniel I. (2000). Further Thoughts on a Sequential Life Testing Approach Using a Weibull Model, Foresight and Precaution, ESREL 2000 Congress, Cottam, Harvey, Pape & Tait (eds), Edinburgh; Scotland; 14-17 May 2000; 2: 1641 - 1647, Rotterdam,: Balkema. De Souza, Daniel I. & Lamberson, Leonard R. (1995). Bayesian Weibull Reliability Estimation, HE Transactions, 27:3, 311-320; USA. Erto P. (1982). New Practical Hayes Estimators for the 2-Parameter Weibull Distribution, IEEE Transactions on Reliability, R-31:2,194-197, USA. Kapur, K. and Lamberson, L. R. (1977). Reliability in Engineering Design. John Willey & Sons, USA. Mood, A. M. and Graybill, F. A. (1963). Introduction to the Theory of Statistics., McGraw-Hill, USA. APPENDK 1
COMPUTING THE E[ln(t)]
The E[ln(t)] will be given by
E[Mt)]=|Kt)i(f]^"J-(f] Making
-0^'
wehave c i u = [ - | ] [ ^ ]
dt;
(Al)
dt
i - ^
When t-> 00, U ^ 0 When t ^ 0, U -> oo. Then, Eqn. (Al) becomes: 00
,
E[bi(t)] = Jin - ^ 0
^
00
00
00
e-U du = ln(G) J e ' ^ du - ^ jln(u)e-U du = hi(e) - i Jhi(u)e-U du 0
0
0
The above integral has to be solved by using any numerical integration procedure, for example, Simpson's 1/3 rule. Remembering that Simpson's 1/3 rule is given by b
.
Jf(x)dr=^^j +4f2 +2f3 + - . + 4f^ + f^^j)-error; Making the error = 0; i= l,2,...n+l, weget
00
fln(u)e""du=^xfln(Ui)e""j +41n(U2)e""2 + -+ln(U„+i)e~"»+« 1. The Enn(t)] will then be 0
E[to(t)] = ln(e) - i X ^ x rin(Ui)e~"i +4to(U2)e""2 +... + ln(Un+i)e~"°+' l;Finally:
E[to(t)] = ta(e) - 1 X | x | | ; [ l n ( U i ) e - " i x(l,2or4)
816
(A2)
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
MAINTENANCE FUNCTIONAL MODELLING CENTRED ON RELIABILITY
F. J. Didelet Pereira^ and F. M. Vicente Sena^ ^Escola Superior de Tecnologia de Setiibal, Rua do Vale de Chaves, Estefanilha, 2914-508 Setubal, Portugal ^Escola Superior de Tecnologia da Universidade do Algarve, Campus da Penha, 8000 Faro, Portugal
ABSTRACT The maintenance functional modelling centred on reliability must integrate the functional system hierarchy. This knowledge allows one to make a bridge to the system physical structure - components or other level of physical organisation. The system functions undergo a process of continuous degradation until their complete failure. To keep functions "up" is basically to develop the required preventive maintenance tasks to carry out upon the system physical structure.
KEYWORDS Maintenance, Reliability, Functional Modelling, Failure, Function, System and Fault Trees
INTRODUCTION Maintenance and its management have an increasing importance upon the productive environment due basically either to the complexity of the technical system or to the interaction between the environment and the system through the links on the boundaries. The relation of our technical system with the one above it, called wider system, must be regarded as of particular importance. The wider system sets the goals, influences strategy and decisions in the
817
technical system, monitoring the system performance and providing the resources to allow system operation. Our model must integrate thefiinctionalsystem hierarchy aUowing one to make a bridge to the system physical structure - con^nents or any other acceptable level of physical organisation. System functions undergo a process of continuous degradation until their complete £ulure. Failure is a term applied when operating capacity is grossly violated. It is partial or complete. The preventive maintenance tasks are applied to the physical structure aimed at keeping system fiinctions "up". The system internal Mure mechanisms must be identified clearly and linked to the potential effects of Mure through system boundaries. The effects and consequences of Mures are important aspects to be taken in consideration when defining preventive maintenance tasks.
THE MODEL CHARACTEMSTICS AND ITS DEVELOPMENT A deep knowledge of conplex techmcal systems allows us to develop a clear understanding of their goals and fiinctions. This knowledge includes all relevant system operational states in accordance with system goals and its operational cycle. Functional modelling is a representation of the knowledge achieved by interpreting a mk of system operational experience and designer intentions. This process builds a system model ki hierarchical terms, relating system goals with the operational requirements of their physical con:q)onents. Functional top-down hierarchy is devetoped until we get clear references to physkal conq)onents. Then the fimctional Mure of any lowest sub fiinction is considered as the top event of a feult tree. Any feuh tree is a systematic and logical process to link the fimctional Mure to its causes components Mures. The application of this method to a complex system feihire process is to devetop many but very small M k trees. However, the Mure propagation up to thefimctionalsystem goals and interfeces must be taken in conskieratioa Specifically, the model is developed sequentially in four steps. The first step is to collect all relevant available informatk>n about the system including operational and Mure data. The hierarchic fimctional analysis allows the identification of certain number of levels in terms of formal and informal rektions. This is a top-down process built in accordance with the method of necessary and sufficient conditions. This "decomposition" process is stopped when there is a clear reference to physical system conponents. To summarise in this step the system goals, principal fiinctions, operational states, operational cycle and subfimctionsare identified and characterised by means of "decon^ation" process. This process must be associated to another in order to identify both the wider system and the environment and their relations to our system. In the second step the Mures of the lowest fimctions identified in step one are considered. Any of those fimctional Mures is the fimctional fault tree top event. This process is shown in a simplified form infigure1.
818
^ W
System Functions
Failure Tree Top Event i
L
System Physical Structure
A Technological Subsystems
System Physical Hierarchy
I
System Physical Components Figure 1: Simplified diagram linking thefimctionalhierarchy to the physical structure At this point the qualitative and quantitative component faOure information is applied. The quantitative information is to evaluate component failure rate, probability of functional failure and component importance criteria (Vesely - Fussel). In the third step the decision criteria process to identify systematically the effects and consequences of component failures upon the system safety and its boundaries, the environment and system availability is developed. This step requires detailed process decision diagrams to support this kind of systematic process. In the fourth step the decision diagrams fi-om step three are updated and developed allowing one to choose the required preventive maintenance tasks based on consequences of Mure, functions affected, potential events and importance criteria.
MODEL APPLICATION The model is applied to an oil tanker with the following basic characteristics; (a) Displacement: 18732 tonnes, (b) Overall length: 163 m, (c) Medium speed: 14 knots, (d) Main engine: Mitsui B&W 8L 45GFC, 7040 BHP at 170 rpm. The modelling process takes in consideration the requirements of ISM Code and other complementary rules and regulations issued for maritime safety by IMG. The ship's typical operational cycle is shown onfigure2.
819
The oil tanker's goal is to carry bulk refined products in safety and quality conditions according to the national and international maritime rules and reguktions at an acceptable cost-benefit to the ship owner. The ship's goal must be kept in mind during all operational states and it is considered the top of the fimctional hierarchy structure (level 0). The principal functions are at level one and for this specific case they are: ship's motion, safety and security, loading/unloading, habitability and production and distribution of electrical energy. Loading in Port
cr>
A
Drydocking
V Unloading in Port
Figure 2: Operational cycle of oil tanker The principal fimction "ship's motion" is executed in the foUowing operational states; (a) Manoeuvring to arrive or left port, (b) Navigation in open sea, (c) Navigation in restricted zones; (d) Anchorage waiting for orders. The last operatk)nal state is a transient state in that the main engine is not used continuously but just to keep the ship in position. To execute this fimction the ship has its operational centre in the bridge in co-ordination with the engine room control. Safety in this fimction means mainly to follow the international convention "COLREG" which define the rights of ship passage, its safe speed, and all actions available to avoid collisions taking in consideration the specific weather conditions. The fimctional analysis of "ship's motion" has three sub fimctions: power, propulsion and ship control. The subfimction"power" has the following characteristics; Ca) In general this subfimctionhas not any global redundant capacity, (b) Engine room speed "slow down" or "stoppage", which in extreme cases will afifect the ship's safe operation and loss of charter, causes Mure of this sub fimction. The assessment of potential ship
820
conditions related to position, weather, traffic schemes and loading will allow identification of the probability of losses, (c) This function needs large quantities of fiiel oil, lubrication oil and high temperatures so there is a potential for hazardous conditions such asfireand explosion. The subfiinction"power" is analysed into the following subfiinctionsat level three; (a) Fuel oil storage and handling, (b) Fuel oil treatment, (c) Fuel oil feeding, (d) Fuel oil combustion control, (e) Conversion of combustion energy into mechanical energy, (Q Flow of mass and energy of exhaust gasesfi*omcombustion; (g) Refiigeration, (h) Lubrication, (i) Starting and engine reversing, (j) Air delivery. For thefiinctionalfailure "fiiel oil treatment" the following con^3onents failures are identified; (a) Fuel oil heater steam trap, (b) Return steam tubefi*omfuel oil heater, (c) Fuel oil separator, (d) Fuel oil filter, (e) Heating coil of fiiel oil service tank. All functional fault trees are evaluated according to thefi-ameworkof the rules in the Fault Tree Handbook issued by the U.S. Nuclear Regulatory Commission. The feuk tree basic events are evaluated with datafi*omthe maintenance records and in certain cases eitherfiromdatabases or similar systems in operation. The safety and environmental consequences of fimctional oil tankers failures are legally considered in rules and regulation issued by IMO and ratified by most countries. The Classification Societies also have a meaningfiil role in that subject. According to this point some aspects of the formal safety assessment techniques are potentially important elements to introduce in this model clarifying the risk involved in ship operations. The consequences of Mures are identified according to decision diagrams. One of these diagrams is shown infigure3. This diagram points out the important question of evident and hidden failures. Hidden Mlures can be a very hazardous aspect and must be controlled in order to avoid the occurrence of multiple Mures. The layout of the ship's technical installations has a lot of redundant elements subjected to hidden Mures. So when there are hidden Mures we also consider the same kind of consequences as above and the probability of multiple failures. Hidden Mures must be considered specifically in both attended and unattended engine room operation conditions. To analyse the consequences of Mures one must take in consideration that the marine operators have mixed duties in operation and maiatenance and so the relevant aspects of human reliability must be considered. 821
Is the Mure mode evident to operator during normal duties?
Figure 3: Decision diagram for the consequences of evident Mures The maintenance tasks are supported by the above decision diagrams and for the oil tanker the following are defined; (a) Conservation and lubrication tasks, (b) Monitoring tasks, (c) Inspection tasks, (d) On-condition tasks, (e) Replacement based on time tasks, (f) Combination of some the above tasks, (g) Corrective maintenance tasks.
822
The scheme of simplified decision concerningfixelinjector Mure is shown in table 1. TABLE 1 FUEL INJECTOR FAILURE Basic Event Importance Criteria Functions Affected by Failure Immediate Potential Events Consequences of failure Maintenance Tasks
Fuel Injector Failure 0,142 Fuel Oil Combustion Control Failure is evident by abnormal change of temperature of exhaust gases and emission of smoke through the ship funnel. Reduction of main engine efficiency - On certain conditions implies main engine stoppage and could produce hazardous conditions for ship safety depending on existing boundary ship conditions. - Task on-condition monitoring using diesel engine monitoring control unit. - Monitoring cylinder exhaust gas ten:^)erature. - According to operational data there is an increase of Mure rate with operating time that means that is justified replacing injectors on a time base.
The subfimction"power" Mure is the most hazardous for safety and availability of oil tanker and the following number of maintenance tasks for that subfimctionwere found; (a) Conservation and lubrication - 9, (b) Monitoring - 54, (c) On-condition - 3 1 , (d) Inspection ~ 6, (e) Replacement based on time - 7, (f) Corrective - 2. The large number of on-condition and monitoring tasks must be pointed out. On the other hand the replacement based on time is only a small part of all tasks. The decision about these tasks is obtained by applying both the WeibuU law to failure and censored times and considering that the shape parameter is increasing in the 95% confidence interval. It is important to point out that all tasks were chosen according to their technical feasibility. However cost considerations would be considered forfixturework. CONCLUSIONS AND FUTURE WORK This model shows a process to provide us a deep knowledge about complex systems pointing out the feedback from real operational conditions. This is a process that needs a lot of information on both system design and operational data.
823
To keq) systemfiinctions"up" one needs to develop a systematic preventive maintenance strategy focussed on physical con^nents that affect those functions. The feult trees are developed for lower sub functions allowing to build small feuh trees but in large number. This can be a procedure to reduce conqjlexity. Decision about the maintenance tasks takes in consideration the functional Mure probability, the component importance criteria, functions affected by con^wnent fiiihire, most prolxible answer of operator in case of &ilure, operation of automatic control or not, existence of hidden conditions and consequences offeihires.Because of the number of activities involved the decision process has a high degree of con^lexity. For future work a methodobgy to increase the informal connection in the fimctbnal structure in a horizontal, up down, and top-down ways must be developed. The concept of hidden Mure must be extended for the functional structure. To improve this model the collectk)n of maintenance cost data is justified in order to base the maintenance tasks on technical feasibility and economy. However if one takes in consideration the cost - benefit context the safety and environmental achievements must not be sacrificed. To get a betterfi-ameworkof functk)nal Mures consequences both the formal safety assessment and human reliability techniques must be introduced. REFERENCES Brauer, D. C. and Brauer, G. D.(1987). Reliability-Centered Maintenance. IEEE Transactions on Reliability R-36,17-24. Dobson, B. (1994). Weibull Analysis, ASQC Quality Press, Wisconsin, USA International Chamber of Shipping and Intematk)nal Shipping Federation. Guidelines on the Application of the IMO Intematbnal Safety Management (ISM) Code Kumamoto, H. and Henley, E.J.(1996). Probabilistic Risk Assessment and Management for Engineers and Scientists, IEEE Press, New York, USA Modarres, M.(1993). Functwnal Modelling of Con^lex Systems Using a GTST-MPLD Framework. Proceedings of the First International Workshop on Functional Modelling of Conq>lex Technical Systems, 21-69. Vesely, W.E., Goldberg, F.F., Roberts, N.H. and Haasl, D.F. (1981). Fault Tree Handbook, U.S. Nuclear Regulatory Comission, Washington, USA
824
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
AN IMPLEMENTATION OF A MODEL-BASED APPROACH FOR AN ELECTRO-HYDRAULIC SERVO SYSTEM A. El-Shanti, Z. Shi, D. Luheng, F. Gu and A.D.Ball Maintenance Engineering Research Group, Manchester School of Engineering, The University of Manchester, Manchester, M 13 9 PL, UK Email: ali
[email protected] Web: www.maintenanceengineering.com Phone: 0044-161-2754308
ABSTRACT For many years, a model-based condition monitoring (MBCM) approach has proved to be an advanced and powerful diagnostic tool. It is especially true for various kinds of control systems. It can not only detect the faults in the plants and their components, but also the sensor faults and faults of new parts. However, there is no application on the electro-hydraulic control system. This paper explores this technique. The first part of this paper reviews the development of the model-based approach. A model of a selected electro-hydraulic position servo system is built in simulink in the second part of this paper. A few types of faults, such as the amplifier offset, backlash, and fatigue of the servo valve are induced into the system. Finally, some fault detection have been carried out by means of a model-based approach.
KEYWORDS Model-Based Diagnosis, condition monitoring, fault detection, Simulink model, Electro-hydraulic servo system, control systems
INTRODUCTION The complexity and degree of automation of technical processes are continuously growing, due on one hand to the increasing demands for higher performance and quality, and on the other hand to more cost efficiency. Along with this development, the call for more safety and reliability is growing more and more important. Model-based fault diagnosis in automated processes has been receiving considerable attention since the beginning of thel970's, Jones (1973). Several survey papers and some books have been published recently on model-based fault diagnosis, Frank (1993) and Patton (1994). However, as reading these literatures, one may find that, although model-based diagnostic approach has made much progress in theoretical research, its application is still not as popular as expected. 825
However, as reading these literatures, one may find that, although model-based diagnostic approach has made much progress in theoretical research, its application is still not as popular as expected. Why does such an advanced approach receive such rare application? Does it work as people have said? All Elshanti, et al. (2001) using Simulink, has given a full demonstration of model-based diagnostic approach. This paper is a further application of this approach. According to the principle of the model-based approach, a model of a selected electro-hydraulic position servo system is built. It is based on two main components, an amplifier and a servo valve, combined with the cylinder and a position transducer, a mathematical model of a close loop controlled system is carried out in Simulink. Some kinds of faults are induced into the system. One is the offset of the amplifier. Others are fatigue and backlash in the servo valve. Some diagrams in Simulink have been supplied hereby. Simulation results will be given to meet the requirement of the model-based diagnosis for the system. Finally, a model-based approach to the detection and diagnosis of faults in electro-hydraulic servo system is successfully implemented in this paper.
PRINCIPLE OF THE MODEL-BASED DIAGNOSTIC APPROACH The concept of Model-based diagnosis is based on analytical redundancy. A process contains analytical redundancy if an input or output can be calculated by using only other inputs or outputs. The principle of the model - based diagnostic approach is shown in figure 1. The analytical model is built to run parallel to the actual system. The model output of a healthy system should be the same as that of the actual system. The difference between the analytical model and the actual system is called the residual. The residual is used to detect if a fault occurs in the system. Due to the difficulty in building a model exactly the same as the actual system, the practical way to detect faults effectively is to design a threshold. If the residual does not exceed the threshold, the system is referred to as healthy. Once the residual exceeds the threshold, some fault may occur in the system, the diagnostic scheme will send out a fauh alarm. This is called ^aw// detection. The next is to allocate and evaluate the fault that occurred in the system, and is called^w// diagnosis, Isermann and Balle (1997). The model-based approach has significant advantages. The main ones are listed below: • Reusable models. Many components and processes can be built models or have their models when they are designed or manufactured. These models can be re-used not only in control but also in fault diagnosis. • Possibility of diagnosing a "new" device. This is prior to any other detecting technique, because the model does not rely on any kind of experience, but only the model of the new device. • Possibility of diagnosing a sensor fault. This is prior to all passive signal-processing methods. In these meliiods, the sensors are taken granted to be healthy, and once the sensor becomes faulty, the signal collected will be distorted. • Work over a wide operating range. It is natural to deal with dynamic and time-varying problems. However, because of the difficulty in the model building and selecting, especially in non-linear systems, the model-based approach has not taken its part in the practice, as it should have.
826
^^C^
Actuator
i
Fault
Fault Process
^
^
Fault
Sensors
N :>
Process model ^^ Residual generation "N^
Fault detection O Fault isolation
Results Fig. 1 The schematic of model based fault diagnosis
MODELING OF THE ELECTRO-HYDRAULIC SERVO SYSTEM The servo amplifier model The Servo amplifier consists of several main control stages including an input stage, control stages, proportional gain, integral gain and a current driver output stage. It requires command and feedback signals, which have opposite polarities. The gain inverter enables either the feedback or command signal to be inverted if they were of the same polarity. The servo amplifier has a 4-20mA converter, which converts a 4-20mA signal to either 0 to +10V or 0 to -lOV depending on the input polarity. Basically, 4mA results in OV and 20mA results in lOV, currents between 4 and 20mA results in proportional voltage between 0 and lOV. The reason why we modelled the amplifier is that we will be able to induce faults in it. Figure 2 shows the Simulink model of the servo- amplifier in the fault free mode.
DC Voltage Source
rH
_[-^h>-^AA, I _j-4Qi AC Vottage Source
Figure 2 Simulink model for the servo amplifier 827
The Servo valve model
The servo valve is the heart of the electro-hydrauHc servo control system. EHS valve is widely used in various control systems to implement precious and heavy load control targets. The servo valve is small in size but has a very high sensitivity. Figure 3 shows the Simulink model of the servo valve.
m ,
,
^*®P
l 0.001 I r \ ^ I »100 I \y^ TranrferFcn3
Gain
,
r>^'=l
A^\
Scope2
Scopel
,
^.
i0.265|
g —
jo^N-
1 Sumi
Gaini
Scope
Transfer Fcnl
Transfer Fen Zero-Pole 1000s (8*100) Gain2
To File
-<M4Figure 3 Simulink model for the servo valve
The overall system model Figure 4 shows the overall electro-hydraulic servo system model. This consists of the servo valve model, the amplifier model, the cylinder model and the feedback. Figure 5 shows the Simulation result of the whole system in the fault free mode.
TR1 step
24
H>^ Gains
Sums
L--J
^
j _
H3>-i Gain4
^^p^
Gairi6
843
(1)
where km represents the torsional mesh stiffiiess and Tp denotes the radius of pinion base circle. The resulting equations of motion describing the coupled torsional and transverse model are given by Eqn. 2 - 27,
h^x +^c(^i -4)+'-.^i2M, -rA -^1 +^2)+ -r,e, -x, ^x,)+F,,,r^, + ^c(^i -0,„yr,k,,{r,e, I A
=0,
(3)
=0,
(4)
+ ''2^12 (''2
0^)
'"3i>3+^.v(y3->^2)+^,(>'3->'M)+^/34=0,
(14)
'"43>4+^,(y4-3^M)+^.(V4->'66)-^/34 = 0 ,
(15)
'Wft^M + ^/.^M + ^/>^M + K U l - ^I) = 0 ,
(16)
'W/.^M + QhXHl + ^*^A2 + ^, (^M - ^1) = 0 ,
(17)
'"/,^M +^6^M +^A^« +^.v(^63 - ^ 2 ) = 0 '
(18)
^"^^64 +^*^M + ^ 6 4 +^,(^64 " ^3 ) = 0 >
(1^)
844
'Wft^fce +^6^*6 + M w + ^ . ^ 6 - ^ 4 ) = ^ ,
(21)
Kyti+^^^2
(23)
+ ^ft>^62+^,^2 - ^ ^ i ) = 0 '
'W^J^W + ^ 6 ^ 3 + ^fe^^M + K (^63 - >^2 ) = 0 ,
(24)
'wJ M + q ^ h ^ + Kyn + ^, (VM ~ ;^3)=0,
(25)
'«/,A5+^AA5+^/,>'/,5+^,v(yA5-3^4)=0,
(26)
'4 ) = 0 .
(27)
and
CONTACT FORCE, FRICTION FORCE AND FRICTION MOMENT The equation for the normal contact force between gear 1 and gear 2 is given by Eqn. 28, and the friction force between the gears is calculated by Eqn. 29, [3], ^12 = ''2^12 (''2^2 ~ ^A
- ^2 + ^1 ) + ''2^12 V2^2 - ^A
- ^2 + ^1 ) '
Fjn=^^n-
(2^)
(29)
Likewise, the normal contact force between gear 3 and gear 4 is given by Eqn. 30, and the value of friction force between the two gears is calculated by Eqn. 31. ^34 = ^34 (^3 - ^4 - '•3 .^ 0.05
0.05
S o c O
•s
•c U-
0
u E 2 -0.05
-0.05
/ 5 10 15 20 Shaft Angle (deg)
10 20 Shaft Angle (deg)
Figure 3. Parameters used throughout the gear simulation, a. Torsional mesh stiffness between gears 1 and 2. b. Torsional mesh stiffness between gear 3 and 4 mesh. c. Load Sharing Ratio between gear 1 and 2. d. Load Sharing Ratio between gear 3 and 4. e. Dynamic friction coefficient between gear 1 and 2. f Dynamic friction coefficient between gear 3 and 4. The resultant signal average (300 averages) of the input pinion velocity 9^ and the corresponding RMS amplitude spectra are shown in Figure 4 with and without the 5mm tooth crack. Figure 5 shows the signal average (300 averages) of the bearing velocity x^j ^ ^ ^^ corresponding RMS amplitude spectra with and without the 5mm tooth crack. Figure 6 shows the output bearing velocity x^^^ averaged over 100 revolutions of the intermediate shaft and output shaft and the
846
corresponding RMS amplitude spectra, when the localised tooth crack was present on the input pinion. 143.2,
P 142.4
At the beginning (t = 0 sec) the first tank is half full (level h = 30 cm), the second tank is filled a little (level h = 10 cm) and the third tank is empty. > The inflow in the first tank is high (pumpl on) > No inflow in the last tank (pump2 off) > All pipes are opened up to t = 20 sec, later on the first pipe (pipe 12) will be blocked. This faulty behaviour can be simulated by a closed valve. Reality
Hybrid Model
(Plant)
Time Informatiofi
Component Specifications and System Structure
Observer
Trajectory Calculation for time slot [ta.tb]
i
; online
Reduced QuallUitlve StE^e Space
Figure 5: State space reduction Figure 6 presents the first two resulting state spaces for the given initial and boundary conditions, beginning ai t = 0 sec. In this figure the reduced state space includes all possible states of the first time slot t = [0,20] sec and the following time slot t = [20,40] sec. The online calculation starts at t = 0 sec with the above described initial conditions. Then the observer calculates all possible trajectories for the first predefined time slot of / = [0,20] sec as a prediction for all possible states. The calculation of the trajectories is performed within the given dynamic model. The result shows, that the level h of the first tank ranges between h = 40 cm and h = 49 cm, of the second tank between h = 10 cm and h = 14 cm and of the third tank between h = 1 cm and h = 2 cm. With this information all impossible situations are excluded fi-om the situation table and the result is a reduced qualitative state space for the given time slot. It is now very easy to read the table. For example in the first state of the reduced table the pumpl is "On", the first and second tanks are in the situation "Filled/Increasing", the two pipes are in the state "Positive" and the pump2 is "Off'. That means that there is a flow from the first tank into the second tank and then in the third tank. The reduced table contains also possible faulty situations such as a blocked pipe.
870
The second reduced state space in Figure 6 is calculated for the following time slot t = [20,40] sec. Considering the boundary conditions the first pipe is totally blocked, that means no outflowfromtankl in tank2. In this case the tankl will overflow within the time slot. This dangerous state is included in the reduced state space and so the overflow of the tank can be recognized in time. Online-Information
Hybrid Model
t « 0 sec: 40 cm "Tani(2~ 10 cm f»Tank3- 1 cm
^»Tank1 "
Pumpi
Pump2
^^m
^Pumpel ^PunweZ
= 011 sOff
System Equations pressure_out_tankl = pfessurejn_pipe12 Pipe12
Pipe23
Trajectory Calculation for time slot [0,20] sec
hTank1=49cm lTank3=
2 Cm
Qpumpi = on Qpump2=Off
Reduced State Space for [0,20] sec FHIeci/increasing Positive
On
Fiiled/lncfeasing
On On
Filled/Increasing
On
Filled^ncreasing Positive
Filled/Increasing
Off
iOn
Filled/Increasing
Fiiied/lncreasing
Off
On
Filled/Increasing Positive
Blocked
Filled/Decreasing
Off
On
Filled/hcfeasing [Blocked Filled/Unchanged
Blocked
Filled^ecreasing
Off
f t - 40 sec: ^ ^Tanki ~ O v e r f l o w ^ '^Tank2 " "^ ^ ' 'Tank3
S^^l^MMSBS
On
Positive
Empty/hcreasing
Off
Blocked Filled/Decreasing Positive
Empty/Increasing
Off
Filled/lncfeaslng Positive
Filled/Increasing
Empty/Unchanged Off
Filled/Increasing
Blocked Filled/Unchanged
Blocked
Positive Filled/Increasing Blocked Fined/Decreasing Positive Fiiied/lncreasing
Sudden appearance of a fault in the system: Pipe12 totally blocked!
Empty/Unchanged Off
Trajectory Calculation for time slot [20,40] sec
Reduced State Space for [20,40] sec
^'^
3 cm
^Pumpl "
|lft)pWri^t19ed
T
Trajectory Calculation for time slot [40,60] sec
Figure 6: State space reduction for the tiiree-tank-system
871
The calculation shown in Figure 6 is perfomied periodically for every given time slot and yields different parts of the reduced qualitative state space. The next step consists of the analysis and evaluation of the reduced qualitative state space in order tofindfaulty or dangerous states. If there is a faulty or dangerous state in the reduced state space, then it is the task of the fault detection to notice and react to this faulty state (e.g. an overflow of the tank) to prevent from danger. Besides the online fault detection the diagnosis of faults and failures andfeultlocalization is also possible. The diagnosis is performed by the backward tracing of the states on the basis of their given transitions. CONCLUSION The major advantage of the hybrid modelling method SQMD is the simple modelling of complex dynamic systems. There are two important aspects. On the one hand already existing mathematical models are combined with qualitative models so that complex systems can be modelled and simulated. On the other hand only the interesting part of the state space is analyzed, that means these states can be evaluated online with low computation power. For the future the model building, analysis and the evaluation should be done in a special SQMD toolbox for Matlab/Shnulink. So the engineer can be supported in a comfortable way and the main part of the tasks can be performed automatically. REFERENCES [1]
de Kleer J. and Brown 5.S.: A Qualitative Physics based on Confluences, in Bobrow D.G. (Editor), Qualitative Reasoning About Physical Systems, North Holland, Amsterdam., pp 7 - 83, 1994 [2] Kuipers B. J.: Qualitative Reasoning: Modeling and Reasoning with Incomplete Knowledge, MIT Press, 1994 [3] Manz, S.: Qualitative Modeling of a Three-Tank-System, Interkama-ISA Tech, DiisseldorC^Germany, Proceedings on CD-ROM, 1999 [4] Manz, S.: Online Monitoring and Diagnosis based on hybrid component models, ICSSEA2000, Paris, 2000 [5] Manz, S.: Fuzzy Based Qualitative Models in Combination with Dynamical Models for Online Monitoring of Technical Systems, CIMCA2001, Las Vegas 2001.
872
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
MEASURES OF ACCURACY OF MODEL BASED DIAGNOSIS OF FAULTS IN ROTORMACHINERY P. Pennacchi and A. Vania Dipartimento di Meccanica, Politecnico di Milano, Milano, Via La Masa 34,1-20158, Italy
ABSTRACT Model based diagnostic techniques can be used successfully in the health analysis of rotormachinery. Unfortunately, a poor accuracy of the model of the fully assembled machine, as well as errors in the evaluation of the experimental vibrations caused only by the impending fault, can affect the accuracy of the fault identification. This can make difficuh to identify the type of the actual fault as well as to evaluate its severity and its position. This paper shows some methods that have been developed to measure the accuracy of the results obtained with model based techniques aimed to identify faults in rotating machines. The results of some first investigations on the capabilities of these methods, carried out using the machine response simulated with mathematical models, are shown in the paper.
KEYWORDS Diagnostics, Fault Identification, Rotor dynamics. Vibrations, Model validations.
INTRODUCTION The early detection of faults and malfunctions in rotating machines can be provided by condition monitoring systems. However, in order to obtain useful information to carry out suitable maintenance actions, a fault diagnosis is required. A first preliminary screening of the most probable types of faults that can have generated an alarm can be obtained with a fault symptom analysis. However, more significant information can be provided by model based diagnostic techniques, Bachschmid et al. (1999, 2000-1, 2000-2). Moreover, these methods allow the fault to be localised and its severity to be evaluated. Usually, the model of the fully assembled machine allows the dynamic behaviour of the rotors, the bearings and the foundation to be simulated. The rotor train can be modelled with beam Finite Elements (FE), while the dynamic effects of the fauhs can be simulated with suitable sets of forces and moments that are applied to the nodes of the FE model. Therefore, the identification of a fault can be obtained by evaluating the system of excitations that minimises the error between the machine experimental response and the numerical response evaluated with the model. Usually, this error is called residue. In order to allow the results of 873
different analyses to be compared the residues are normalised. Weighted least square methods can be used to identify the equivalent forces and moments that simulate the faults. Although the vibrations of the shafts along the spans between two adjacent supports can give very useful information to identify the machine faults, usually, in real machines, only shaft and support vibrations measured at the journal bearings are available. This requires more careful fault identification analyses to be carried out. Unfortunately, also in absence of faults the rotating machines are subjected to vibrations that, usually, are caused by a residual unbalance of the rotors as well as by misalignments of couplings and supports. These excitations cannot be easily modelled as they are unknown. Therefore, fault identification techniques require to evaluate the transient vibrations of the machine obtained by subtracting the vibrations measured in normal state fi-om those measured after the fault occurrence. If the non-linearity of the system is negligible, these transient additional vibrations represent the machine response caused only by the fault. However, if the two speed transients occur in different thermal conditions of the machine or if other factors, in addition to the malftmction, affect the system vibrations, the accuracy of the results of the fault identification can be poor. Similar effects can be caused by the presence of noise in the vibration signals. Moreover, the accuracy of the results of model based techniques can be significantly influenced by the care with which the model of the fully assembled machine has been developed. Usually, a preliminary careful tuning of the model is required before applying it for detailed investigations. Owing to a poor accuracy of the experimental response, as well as of the model, the normalised residues associated to different identified faults can be fairly similar. This can make it difficult to diagnose the type of the actual fault as well as to evaluate its severity and its position. This paper shows some methods that have been developed to measure the accuracy of the results obtained with model based techniques aimed to identify faults in rotating machines. The information provided by these methods can be very useful to evaluate which of the identified faults has the highest probability to be the actual one. The results of some first investigations on the capabilities of these methods, carried out using the machine responses simulated with mathematical models, are shown in the paper. Although only simulated responses have been used, this investigation has allowed a useful sensitivity analysis of the developed methods to be carried out. Successful results already obtained by the analysis of experimental responses will be shown in a future publication.
MEASURES OF ACCURACY OF THE RESIDUES As said above, the error between the theoretical vibrations obtained with the model and the experimental vibrations is called residue. A normalised value of the residue, Su can be obtained using the following expression:
If
^ex
'^ex
where * indicates complex conjugation, Xex is the vector of the experimental vibrations, while X/^ is the corresponding vector of the theoretical vibrations obtained with the model. The machine fault is identified by minimising the residue; therefore, the most probable fault should be that one with which the lowest residue is associated. As the identification procedure can be time consuming only the experimental data collected for a limited number of suitable angular speeds of the machine are analysed. Sometimes, this strategy is necessary also to avoid to use an overabundance of repeated observations in the identification procedure as this could cause numerical problems or identification errors. Obviously, different selections of the angular speeds, that is different subsets of experimental data, can give quite different results of the fault identification. For instance, if the model does not simulate the response of the system very well near the critical speeds the selection of vibration data measured in speed ranges near the resonances should be 874
avoided. Anyhow, the excitations obtained with the identification procedure can be used to evaluate the system response at any angular speed associated with the experimental data. This allows a further residue estimation, 82, to be evaluated. Sometimes, depending on the choice of the data used for the fault identification, although a very low value of the residue £\ is obtained, the residue estimation £2 is significantly higher. This is a symptom of a poor accuracy of the fault identification. This can be the consequence of a wrong selection of the type of fault as well as of a poor adequacy of the model. Further indexes allow the above mentioned causes of very different values of the residues £\ and £2 to be discriminated. As the object of a fault diagnosis is to identify the actual machine malfunction as well as to simulate the system response, it is important to obtain a low value of both the residues £\ and £2. Usually, both the residues £\ and £2 are computed considering the errors evaluated at different degrees of freedom and at different machine angular speeds. Therefore, they can be called global residues. On the contrary, further residue estimates can be obtained by selecting the vibration data evaluated at each single angular speed or at each single degree of freedom (d.o.f). These residue estimates can be denoted with ^o) and £"dof respectively. They can be very useful to show the dependence of the error on the angular speed and the measurement point. In the case of an ideal identification of the fault the residues should be scarcely affected by the speed value and the d.o.f. Owing to this, the analysis of the curves of the residue fo vs. the angular speed, as well as the curve of £dof vs. the degrees of freedoms, can give very important information on the accuracy of the identified fault and the adequacy of the model. In general, the ratios between the standard deviations of the residues £^^ and ^^dof and their respective mean values represent good measures of the identification accuracy. The residues are a normalised evaluation of the error between theoretical and experimental vibrations. With reference to this, it is important to consider that the amplitude of the machine vibrations can be quite different depending on the angular speed and the measurement point. Therefore, the analysis of the curve of the absolute error vs. the angular speed can emphasises some information that are masked by the curves of the normalised residues. In the end, interesting results can be obtained by a correlation analysis between theoretical and experimental data. The real and imaginary parts of all the theoretical and the experimental vibrations can be located into the two real vectors X//, andXe^c, respectively. Then, the Modal Scale Factor (MSF) and the index called Modal Assurance Criterion (MAC), Ewins (1984), can be evaluated as follows: MSF = xlx,Jxlx,, \YT
Y
MAC = - ^
(2) 2
L
(3)
In general, the two indexes MSF and MAC are used in modal analysis and model updating; however, they can be applied also to many other problems. If Xth coincides with JC;c the value of the indexes MSF and MAC is 1. Anyhow, as the identification procedure is based on a least square method, in this application the MSF value is always very near to 1. On the contrary, the MAC index gives a measure of the dispersion of the data from the straight line whose angular coefficient is the MSF value. In addition, a linear regression analysis of the data contained in the vectors Xth and Xex can be carried out. The difference between the angular coefficient of the regression line and the ideal unity value is a further measure of accuracy of the fault identification. In the end, an additional information is represented by the value of the standard deviation of the errors obtained with the linear regression analysis.
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ANALYSIS OF SIMULATED RESPONSES DUE TO FAULTS The validation of the method developed to measure the accuracy of the results of the identification techniques has been carried out by simulating the experimental response of a rotor train composed of a high-intermediate pressure turbine (HP-IP) and a low pressure turbine (LP). The system response has been evaluated with a model of the fully assembled machine. Each rotor was supported on two oil-film journal bearings while a rigid foundation of the machine has been considered. Figure 1 shows the Finite Element (FE) model of the rotors. The supports have been numbered from #1 to #4. The first balance resonance of the HP-IP turbine was nearly 1500 rpm while the second critical speed was nearly 2900 rpm. In order to simulate an ideal experimental response of the system due to a local bow of a rotor, two equal but opposite bending moments have been applied to the ending nodes of a finite element of the mesh located near bearing #2 (Figure 1). The machine response has been evaluated in vertical and horizontal direction in the range from 400 rpm to 3000 rpm. Only the radial vibrafions evaluated at the four journal bearings have considered in the faults identification procedure. These degrees of freedom have been numbered from one to eight, starting from bearing #1 to bearing #4. 1X Horizontal vibrations
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i Brg.#2 HP-IP tublne
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Fie. 2 Performance in fault free situation
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a
^
m
_^^
yF^-'-yi"^^ } denote principal components of the condition information obtained at ti, where m is the number of the first few key principal components, and yi^\ yPK-.-yl'^^ are statistically independent each other. • Yi=^{yuyi.iyyi.2y>»,yyi} denotes the history of cumulative key principal components up to U, the current monitoring decision point. • p(xi\ Y'l, ti) denotes thepdf. ofxi conditional upon Yi. • P(yi^^\^i) denotes the pdf. ofyp^ conditional upon xi. The relationship between xt and x,./ is as follows: ^
\ ^,., - (/, - /,_,), if x,_, > t. - r,_,, [not defined, otherwise
^^^
The relationship between y,^-^ and Xi is described \>yp(yF\Xi), which is yet to be determined. We wish to establish the expression of p(xi\ Yi, U ) so that a consequential decision model can be constructed on the basis of such a conditional probability. It can be shown, Wang and Christer (2000), ihdXp(xi\Yi, ti) is given by AyilYi.|,/,j where using the chain rule, the joint distributions, pfjc,, y, |Fi U) and/?5',|Fw, ti.) are given respectively by Wang and Christer (2000) as
pU.y, I Yi.„0=p(yi U„Yi.„/,)p(Ar, I Y„,o=P(yi \xMx^^..^,h) P(y< I YM,&,. where
(5) (6)
piy, I ^,)=P{yf\y?' -yT' I x,)=p{yf' \ xMy?' I Jf,)-/'(x'"' I ^,)
f,
P(J^,-,|Yi.,)^,-,
(7)
J , p{x,,,\Y.,_,)dx,.,
It can be seenfi-omequations (5)-(8) that \fp(xo\Yo) 2indp(y!'\xi) (j=^l may be determined recursively.
m) are known equation (4)
3, Establish the distributions ofp(xo\Yo) andp(yP^\Xi) To make equations (5)-(8) computable, we need to specify the distribution forms for p(xo\ Yo) and p(yF\xi). Since Yo is not available in most cases so that we can set p(xo\Yo) =p(xo). Candidates distributions for p(xo) could be Normal, Weibull, Lognormal or Gamma. For most mechanical items such as bearings and gears, an appropriate choice for the delay time distribution is the Weibull distribution. The distribution of >'r ^=7 m) conditional upon jc, can be chosen as a normal or other distribution. Here we also select Weibull distribution as the distribution of yi^^ conditional upon JT,, that is
892
The conditional relationship betwQQnyP and ;c, is established by the following functions:
- 4 ^ = A(j) + BU)e-'^'''''
j =l
m
(10)
where y4(/;;, 509 and CQ) are parameters to be estimated from the data. This is, in fact, established a negative correlation between >^/^'^ and JC, as expected in condition monitoring practice. 4, Estimation of the parameters We wish to estimate the parameters of the delay time distribution of item andAQ), B(j), C(j) and Tj(j) based upon the observed life data and the available condition monitoring readings using the maximum likelihood method. The likelihood function is written as L
)
(11)
Wherej!?(^.|o9 denote probability density of observing • given that o has occurred, L is the number of items tested, xu is the observed information at tt and «/ is the last monitoring check for items /. When the initial delay time distribution of the items is WeibuU, after some manipulation, the likelihood function of equation (11) becomes L
g
m
L
til
j=\
l=\
1=1
-t' -
r(y)/Mf/u«r/v-^(^^-^>;,= 3/x,)= r - p = - €
(15)
2^'n f e"
2a^
dZdx,
where Z^ depends on the value of y,. The likelihood function based on equation 17 and 18 is as follows, iZ-{A+Be ^"JJ 'J")y
(19) where m is the number of components. Maximising equation 19 yields another problem as Z, is positively correlated with A, and therefore we have to use a profile likelihood with a range of fixed Zj values. The result is given in Table 1.
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TABLE 1 ARTIFICAL DATA'S PARAMETER ESTIMATION RESULT A
B
C
0.594 3.057 0.029 L669 2.560 0.029 L828 8.838 0.029
^1
1 2 3
^2
^3
a
L782 2.657 OMll 2.610 3.343 omii 5.104 7.637 (^mii
P
(7
1.2 0.141 1.2 0.118 1.2 0.409
F -31.373 -31.373 -31.373
F is the log likelihood function value. Since the number of parameters to be estimated is the same, the value of F is a good indicator of the goodness of fit. From table 1, it can be seen that there is no difference, so we simply choose the first set result, that is when Z, = 1. To test the model formulation, we re-used this set of parameters in a simulation, and generated a sample of simulated expert judgements, and then re-estimated the model parameters. Comparing the parameters from this simulated sample with the above result, we found that the first group of parameters estimated when Zj = 1 is reasonable and this also partly confirmed our model formulation. For estimated parameter values using simulated data, see Table 2. TABLE 2 SIMULATION DATA'S PARAMETER ESTIMATION RESULT A
B
C
0.771 2.898 0.037 1.849 1.91 0.037
^1
1 2
^2
^3
1.482 2.455 2.317 2.957
a
P
G
F
omii ()mii
1.2 1.2
0.193 0.127
-16.047 -16.047
Real data This data set comes fi'om a maintenance data set of pumps of a large soft-drinks company in England, Wang et al (2001). The expert judgement is made of 4 integer numbers ranged fi-om 1 to 4 as shown below. (1) The pump is operating normally. (2) The pump is operating and shows signs of deterioration. It is advisable to take some preventive action at the next planned maintenance. (3) The pump is operating, but requires immediate attention. (4) The pump has failed. In this case, there were maintenance interventions between condition monitoring for which the modelling is beyond the scope of this paper. For simplicity, we treat the data as maintenance free data, that is when there is a maintenance, we treat it as a renew point. Because in this example, no expert judgement 4 is available, and expert judgement 1 is not our concern, so we are left with only expert judgements 2 and 3 to be used in our model, and we only need Z, to specify the interval between >^j = 2 and 3. We fit the model into these data, after some manipulation, we obtained the estimated parameter values shown in table 3.
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TABLES REAL DATA'S PARAMETER ESTIMATION RESULT A 0.795 2.0 2.029 4.33
B 0.381 14.18 1.93 11.78
C 0.005 0.132 0.006 0.946
^1
1 2 3 4
a 0.007 0.007 0.007 0.007
P 3.162 3.162 3.162 3A62
a 0.087 0.022 0.46 0.869
F 45.7 45.7 45.7 43.226
As before F is the log likelihood function value. We cannot really choose a set of the results from F, since they are so close. It seems that the third set of the results with Zj =3 is better, so we apply this set of results to our model. We seek to plot the predicted residual times of the pumps at various monitoring points based upon estimated model parameters. The functional form of p(x, /Y,)is given in equation 9. The predicted residual time distributions at some monitoring points given the history information based upon the pumps data are plotted in Fig.l.
300
time of checking
residual time
Figure 1: Predicted conditional residual time of three pumps From Fig.l, we can see that the predicted residual times are reasonable, which give us confidence of the model established. The important point here is the ability of the model to pick up individual remaining lives given both the expert judgement history Y and the current age. At the beginning of the monitoring all pumps are assumed to follow an identical Weibull delay
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time distribution, namely P{XQ). AS the time goes and more expert judgements become available, we can observe completely different patterns of the pump lives which are indicated by our model.
CONCLUSION This paper reports the development of a model using expert judgement as leading information to predict the residual time distribution. We used the normal distribution as the distribution describing the relationship between the expert judgement and the residual time. The discrete type of expert judgement is transformed into a continuous distribution by dividing the range into various interval each corresponding to a particular judgement category. The model was fitted to simulated and real world data, and the results are satisfactory.
ACKNOWLEDGEMENTS The research reported here is partly supported by EPSRC under grant number GR/M96582.
REFERENCE 1. Christer AH and Waller WM (1984). Reducing production downtime using delay-time analysis, y. ORS, 35,499-512 2. Wang W and Christer AH (1995). A simple condition monitoring model for a direct monitoring process. Euro. J. Opl Res., 82, 258-269. 3. Wang W, Christer AH and Sharp JM (1996). Stochastic decision modelling of condition based maintenance, proceedings of COMADEM96, 16-8 July, 1996, Sheffield, (Rao B.K.N, Smith R.A., and Wearing J.L., Eds.), Sheffield Academic Press, Sheffield, 1175-1184,1996 4. Wang W and Christer AH (2000). Towards a general condition based maintenance model for a stochastic dynamic system, J. Opl. Res. Soc, 51:145-155 5. Wang W (2001). A model to predict the residual life of bearings given monitored condition information to date. Submitted to IMA J. of Management Mathematics. 6. Wang W, Scarf PA and Smith MA (2001). On the application of a model of condition based maintenance. J ORS, 51, 1218-1227
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
OPTIMISING COMPLEX CBM DECISIONS USING HYBRID FUSION METHODS
R Willetts*, A G Starr*, D. Banjevic**, A.K.S. Jardine**, A.Doyle* •Manchester School of Engineering Maintenance Engineering Research Group (MERG), Oxford Road ManchesterM13 9PL,UK
[email protected],
[email protected] http://www.maintenance.org.uk **University of Toronto Department of Mechanical and Industrial Engineering, CBM Laboratory 5 King's College Road, Toronto, M5S 3G8, Canada baniev(a),mie.utoronto.ca. iardine(a).mie.utoronto.ca http://www.mie.utoronto.ca/cbm ***WM Engineering Limited Manchester Science Park, Pencroft Way Manchester, Ml5 6SE Tonv(g)wmeng.co.uk http://www.wmeng.co.uk
ABSTRACT Maintenance actions must be predicted, planned and integrated into a company's overall production and maintenance schedule. This can be best obtained through the successful identification of failure modes and the subsequent development of a cost-effective maintenance strategy. Computerised maintenance management systems (CMMS) have traditionally based maintenance actions upon changes within the trend of predefined parameters obtained from condition monitoring. However, the identification, monitoring and subsequent fusion of data from key parameters can provide greater confidence in maintenance decisions. This paper presents the results of a case study jointly undertaken between the Maintenance Engineering Research Group (MERG) of the University of Manchester and the CBM Laboratory of the University of Toronto. The aim of the study was to use the EXAKT™ and MIMIC 2001 software packages to establish key vibration monitoring parameters for equipment within a paper mill and aid the maintenance optimisation process.
KEYWORDS Condition Based Maintenance, Integrated Systems, Key Performance Indicators, Optimising Maintenance Decisions.
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INTRODUCTION The decision to use a particular maintenance policy often depends on the experience and preferences of the engineer defining the programme. However, these poHcies may not be the most effective in terms of minimisation of cost and production disruption for the defined failure modes. An efficient system needs to be capable of defining the most effective combination of monitoring parameters by the ftision of all available information. This paper will describe a case study where data from an existing CMMS is converted and combined with failure history and age concerns to produce an optimal replacement interval. A description of how the Data Fusion Framework can be used to aid this integration process is also included. CONDITION MONITORING - THE NEED FOR INTEGRATED PARAMETERS. Condition based maintenance (CBM) is a strategy that has received a great deal of attention in both the research and industrial communities. Its underlying concept is to provide the prediction capabilities of failures prior to their occurrence. To achieve this, CBM makes use of condition monitoring which establishes the present condition of the plant under investigation by the regular monitoring of defined parameters. By trending the data produced, the normal safe working condition of the plant item over a period of time can be established and thus any deviation from the norm can be identified. Kennedy (1998) states however, that the output of condition monitoring is data. It is the interpretation and combination of this data with engineering knowledge that allows maintenance decisions to be made. These decisions are seldom based upon the output of a single parameter. More often they are based upon the fusion of the engineers knowledge and experience with the analysis of the relationships between groups of parameters. However, determining these relationships can be a complex process as there are numerous monitoring techniques available, each with their own data format and relative complexities. Data Fusion One recently developed model that could aid the integration of measurement parameters is the Data Fusion Framework developed by The Joint Directors of Laboratories (JDL) Data Fusion Working Group. This fi-amework was originally developed for use within the US defence network, for the identification and classification of enemy targets. Hall and Linas (1998), described the four levels of fusion in this framework, these are: 1. Object refinement 2. Situation refinement 3. Threat refinement 4. Process refinement Hall and Garga (1999) described level 1 data fusion as a process that combines information to form a representation of an individual object. This process is undertaken by completion of four key functions, these are: 1. Alignment of the data into consistent sets of units and co-ordinates. 2. Estimation of the object attribute and descriptions. 3. Extension of the object's attributes and estimation of its type. 4. Refinement of the estimated description of the object. Level 2 data fusion develops a description of the objects in relationship to their environment. Level 3 predicts future events and alternative hypotheses for the defined objects. Level 4 fusion is often associated with sensor management and forms decisions based upon the estimates provided by the previous levels, whilst monitoring the entire data fusion process to increase the performance of the system. The data fiision framework is not a definitive structure but a series of guidelines that the development team can base their ideas upon. Hall and Garga (1999) state that one of the main 910
problems facing the engineer attempting a data fusion project is the accurate mapping of the 'problem domain' to the 'solution domain'. One of the main reasons for this could be that, depending upon the system requirements, fusion can either be undertaken at all levels or at a single stage. Intelligent Systems A number of systems have been developed in both the research and industrial communities that have attempted to not only determine the relationships between the different parameters but also perform diagnostics. Artificial Intelligence (AI), particularly expert systems and neural networks, has proved to be a popular environment for this research, however more traditional environments such as muhi-variate statistics have also been used. For example, Harris and Kirkham (1997) described a system that used a combination of neural networks and expert systems to perform diagnostics on generic forms of bearings. Smith (1996) described a system in which a group of neural networks were used to combine data from three sensors within a CNC machine to establish five different types of tool wear. Taylor and Maclntyre (1998) described a generic diagnostic system based upon the fusion of different sensor outputs. The industrial community has also developed several systems, for example Trave-Massuyes and Milne (1997) described possibly one of the most successful of these systems called the Tiger system. This is an on-line model-based system designed to monitor the operating conditions of gas turbines. The MAINTelligence system^ works at the generic level and allows the data from different monitoring systems to be combined within a diagnostic system. However this system requires extensive knowledge of the process under investigation, as the knowledge base needs to be up-dated for each new fault. Andersson and Witfelt (2000) described the ADVISOR system, as being based upon the combination of neural networks and expert systems to provide a diagnostic aid for the analysis of failures within rotating machinery. Multivariate statistical systems The EXAKT^'^ system developed by the CBM Consortium at the University of Toronto, unlike the systems is not a diagnostic tool. It attempts to optimise the maintenance interval of groups of machines by modelling the relationships between different parameters and the probability of change. This modelling is denoted as the Proportional Hazards Model (PHM) and allows the system to produce a statistical model of the plant item based upon the combination of the parameter history and the risk of failure. To enable this combination a Markov chain is used to describe the behaviour of the condition over time. This is then used with the PHM, the working age of the units, and costs incurred by failure and replacement to produce a chart that shows the remaining optimum life of the defined machine. This type of system could be the next step on from a traditional integrated CBM system. Computerised maintenance management systems CMM systems have been used throughout industry for a number of years. There are numerous systems available on the market, with varying levels of functionality. MIMIC 2001 developed by WM Engineering is unusual in that it combines a work control system with an integrated CBM system. MIMIC 2001 is a modular system comprising of: • CBM and Asset Manager - allows the user to define and manage the assets held within the plant; • Work Control Manager - allows jobs to be scheduled based upon the reports produced by the Asset Manager; • On-line Monitoring - allows on-line monitoring of defined parameters within the plant; • Report Manager - allows reports to be produced on different groups of parameters. These reports can range from parameters on alarms to job histories for each parameter.
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The CBM and Asset manager makes use of a function called the Asset Hierarchy, which is used to uniquely identify measurement parameters. Measurements can be trended to form a time-based history of the parameter, which when analysed by maintenance personnel can result in actions, such as work requests. Although MIMIC is very comprehensive in its functionality there is a perceived need for improved integration within the different internal modules and to other management systems.
CASE STUDY This case study was undertaken on condition monitoring data collected over a 10 year period from a variable speed paper mill, from one of several leading monitoring systems presently used within the plant, ranging from on-line production control to manually collected condition monitoring data. The database used in this case study was structured using the Asset Hierarchy of the MIMIC system to reflect the different processes and areas of the plant and uniquely identify each functional unit. Readings were taken at predefmed intervals to monitor changes in fault conditions, for example looseness, bearing damage, harmonics and overall vibration levels. Extracted Data The experimentation was based upon the data collected on 18 identical units, each with 8 measurement parameters relating to different failure modes. These parameters are used to monitor vibration signals in both the vertical and axial directions. Table 1 shows these parameters together with a measurement description. There was an average of 140 data points for each parameter with an average monitoring interval of 21 days. TABLE 1 Description of Measurement Parameters Parameter
Direction
Failure Mode
Units
Overall Mesh Harmonics Acceleration
Vertical Vertical Vertical Vertical Vertical Axial Axial Axial
Overall Vibration Gear Mesh Harmonics Acceleration High frequency bearing damage Overall Vibration Gear Mesh Harmonics
Velocity (mm/s) Velocity (mm/s) Velocity (mm/s) Acceleration (g) ESP Envelope (g) Velocity (mm/s) Velocity (mm/s) Velocity (mm/s)
ESP Overall Mesh Harmonics
A total of 64 defined failure events were obtained from the job history stored within the MIMIC work control system. These events were classified as either failures (denoted EF) or suspensions (denoted ES). Failures were defined as events directly caused by an unexpected failure within that machine. Suspensions were defined as events that are not directly caused by a failure, for example lubrication, preventative maintenance etc. This resulted in 7 events being classed as failures and the remainder being classed as suspensions. The majority of these suspensions occurred due to preventative maintenance actions being undertaken either on a fixed time basis or as the result of another failure. Building models within the EXAKT^'^ system A number of steps are involved in the building of an EXAKT^'^ model: 1. Data preparation, which converts the original measurement and failure history into the EXAKTTM format. 2. Establishing the optimum combination of parameters (called co-variants) which are the most relevant for the defined groups of machines. 3. Defining a series of equally spaced bands for each co-variant from zero to an upper level, which encompasses approximately 98% of the measurements. These bands are used to form 912
the basis of a probability matrix, which defines the probabihty of the measurement changing state in the next monitoring interval. From these stages a decision model is built, which combines the above information with the costs of preventative maintenance and failures. The model calculates a number of features that are used to predict the optimal replacement period. The primary feature is the cost function, which is based upon the total costs that would be incurred at different risk values. This is then used to determine the optimal replacement interval at the least cost and an analysis of the remaining life for each unit can be performed. Data Preparation A major problem, not only in the condition monitoring world, but in system integration in general, is one of communication between different systems. To enable this case study to be completed, the original data from the MIMIC was manually converted to the EXAKT^"^ format. However, the software needs to have a complete data set to allow any analysis to be undertaken. This means that any missing data has to be predicted and included. During this case study two different but common situations were identified: 1. Only one or two data points were missing - these values were interpolated from the surrounding points to give an estimated value. 2. Large number of missing points - an average value based upon the readings observed in the other identical units was calculated. Definition of optimum number of parameters It was uncertain if all of the 8 parameters originally being monitored for each machine were statistically significant for the identified failure modes within the machine. To determine their significance a Weibull analysis was undertaken on all possible combinations. A comparison of these analyses showed that the best model for the defined failure modes would be based upon the combination of vertically monitored acceleration and axially measured gear mesh. Definition of state bands The MIMIC system, like the majority of CMM systems, makes use of manually set fixed alarm and warning levels to signal the change in a parameter's state. These levels are often based either upon the experience of the user or the baseline condition of the machine. The EXAKT'^'^ system is somewhat different in the definition of these levels because instead of fixed warning and alarm levels the system defines a series of state bands which define the different conditions the plant item can be in. These bands are used within the Markov chain to determine the probability of the plant item changing state in the next monitoring period. These bands can be either automatically determined or manually altered by the analysis of the spread of the measurements against working age to improve the defined model. Table 2 shows a comparison of these bands with the calculated bands and the MIMIC alarm and warning levels. TABLE 2 Comparison of MIMIC alarm and warning levels and the EXAKT'^'^ state bands EXAKT Band 1^' •^nd -jrd
4'h
Calculated Acceleration Gear Mesh 0.804 6.2 1.606 2.408
MIMIC Manually Set Acceleration Gear Mesh 0.5 1 2 1.0 1.5 3 5 2.4
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EXAKT Decision Model The decision model is based upon the combination of the above stages with the cost of either replacement at failure or for preventative maintenance. It is estimated that an unexpected failure of these units would resuh in a 24 hour shutdown of the entire plant, incurring a cost of £120k; a fixed time replacement (preventive maintenance) would result in a 4 hour shutdown with an incurred cost of £20k. From this information a cost function was determined that identified the incurred cost per day at an optimal risk factor for that group of machines. Figure 1 shows the cost function calculated during this study, whilst table 3 shows a comparison between employing the optimal replacement policy and a replace only at failure (ROOF) policy. This comparison shows that the expected time between replacement would decrease from 3081.43 days to 1765.23 days but there would be an expected saving in incurred costs of 37.2%. TABLE 3 Summary of cost analysis for the defined model Cost (£/Day) Optimal Policy Replace at failure Saving
24.45 38.94 14.49 (37.2%)
Preventive Replacement Cost (£/Day) 8.71 (35.6%) 0 (0.0%) -8.70487
Failure Preventive Replace on Failure Replacement Replacement (%) (£/Day) (%) 23.2 15.7505 76.8 (64.4%) 100.0 38.943 0.0 (100%) 76.8 23.1924 -76.8
Expected Time Between Replacements (Days) 1765.23 3081.43 -1316.2
j|_jjjgj_xp| 80
Cost Function Preventive replacement cost Replacement at failure cost
70
f 40d em Risk Ip/cf] (iisizarci^K)
Figure 1: Cost Function showing Costs that would be incurred for the optimum policy and the replace only on failure Justification of optimal replacement policy using cost analysis To ensure that the defined model is predicting the correct information a manual analysis of the results obtained was undertaken. This comprised of four stages, these were: 1. Obtain information from model; 2. Analyse decision reports for each unit; 3. Build report code matrix; 4. Cost analysis. Step 1 - Information from model The cost analysis requires a number of different variables to be obtained from the defined EXAKT model. Table 4 summarises these variables and defines the values obtained from the model. 914
TABLE 4 Variable for use in cost analysis Variable Name Optimum replacement policy cost Expected time between replacement Replace only on failure cost (R.O.O.F.) Expected savings from optimal
Units £ Days £ £
Value 24.45 1765.23 38.94 14.487
Step 2 - Analyse decision reports for each unit Figure 7 shows a typical decision graph for one of the units in this case, unit number 18. Analysis of the decision made at the end of each of these histories can be used to validate the developed model. The present decision of this graph is 'don't replace', however the chart shows a number of points in the 'replace immediately' region, which is an indication of an event occurring during this history.
wmm.
aaHi W^0ismmmmnk I^^el^lon
S0O
lOdO
1600
"^mmrm A0B «* loss tdi
:^M
2000
4^ 0$§4B7^OCAMesh
Figure 2: Decision graph for unit 19AD showing a decision of 'don't replace' Step 3 - Build report code matrix There are 12 different reporting codes used within the system, which are defined based upon the interaction of the replacement decisions with the defined trend state at the end of the history, table 5 shows these codes. TABLE 5 Decision Matrix - showing available decisions and number of decisions observed within the model
Replace at current record Replace at previous record Replace if inspection point were interpolated Don't replace
Failed 0 3 0 4
915
Suspended 0 0 0 40
In Operation 0 0 0 17
Step 4 - Manual cost analysis The actual calculations used to justify the EXAK'H^ model are dependent upon the codes reported in the decision matrix (table 5). In this case study, the cost analysis was based upon a comparison of two theoretical daily costs per day and the actual cost per day. These theoretical costs are based upon two situations the first is failure and suspended history events only and the second contains failure, suspended histories and still in operation (suspensions). The actual cost per day is based upon the total costs incurred if both the failures and preventive maintenance actions were allowed to occur. For this case study the actual cost per day TAC = £36.95. To enable the theoretical costs to be calculated for each of the two given situations the total working age for all of the decision codes needs to be calculated. Table 6 defines these ages in relation to the decision codes. TABLE 6 Total working age for all histories at the defined decision codes Failed 0 1434 0 3645
Replace at current record Replace at previous record Replace if inspection point were interpolated Don't replace
Suspended 0 0 0 29641
In Operation 0 0 0 18857
There are two different theoretical daily cost calculations possible for the cost analysis. These costs are defined in table 7, which shows both situations. TABLE 7 Calculations of theoretical cost per day for the two given situations
Decision Failure Suspension In operation Totals Daily Cost/day
4 43
Casel Total Cost (£) 480000 860000
47
1340000
Num
Working Age (Days) 1434 33286 34720 38^^59
Num 4 43 17 64
Case 2 Total Cost (£) 480000 860000 340000 1680000
Working Age (Days) 1434 33286 18857 53577 31.36
Comparison of savings both actual and theoretical Table 7 shows the calculated costs that would be incurred if the EXAKT"^"^ maintenance interval was used. To determine the relevance of these daily costs per day, the percentage change of the second theoretical case against the actual total cost (TAC) was calculated and then compared to the percentage change of the optimal policy cost (obtained from the cost function, table 3). Table 8 shows that if the optimal policy was used the company could possibly see a 33.84% saving in maintenance cost. However, when the decisions were analysed an actual saving of 15.15% was evident. TABLE 8 Comparison of optimal policy and actual EXAKT decision Decision Actual decisions Optimal policy
TAC (£) 36.96 36.96
Theoretical Costs (Case 1) (£) 31.36 24.45
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Saving, (%) 15.15 33.84
DISCUSSION OF CASE STUDY Maintenance actions are seldom based upon the change in a single parameter. Often the decision to undertake maintenance is based upon a change in the relationship of key parameters. However, determining which of the defined parameters are key for the overall unit can be a difficult process. The statistical model developed within this case study attempted to identify these parameters and subsequently optimise the CBM process. The results of this case study identified a number of features that need further consideration. The first of these was that the number of parameters presently being monitored were found to be not well related to the machines under investigation; only 2 of the original 8 parameters were used for the modelling process. The most probable reason for this reduction is that the parameters are defined based upon identified failure modes, for example, misalignment and bearing damage. A requirement of the system is that the third party database contains information regarding the type of event that resulted in the end of that particular history. Therefore if that type of failure mode has not been evident in the event history of the machine prior to the modelling process, its significance on the model would be greatly reduced, but this could alter if any of these failure modes subsequently occur. At present the system cannot cope with this type of novelty detection and therefore requires a model to be constructed for every new failure mode. The EXAKT''''^ system works by defining 2 different failure modes, namely failures and suspensions. The classificafion of these failure modes can be subjective depending upon the failure history recorded within the traditional CMM system. It was found that a large proportion of the maintenance actions were opportunistic, either when a failure was identified in one of the other units or when the plant was on a shutdown period. Analysis of the decision models showed that 4 identified 'failures' were actually suspensions. It was further found that a number of the identified 'suspensions', which were defined as being 'in service' at the present interval, showed a failure at some point prior to the end of the history. The most likely reasons for this feature are inaccuracies in the reporting of work undertaken, the time at which the actions were first reported and the time when the machine was returned to service. The cost analysis was undertaken upon the developed model to show the difference between the different theoretical daily costs and the present actual costs using the optimal replacement policy. The difference in possible savings could be due to a number of reasons. The most probable reason is the way in which events are classed most evidently when the original work history is either incomplete or inaccurately reported. The comparison of expected to actual saving showed that the model developed is relatively accurate in its construction. However, the amount of savings are not only dependent on the accurate building of the model, but also on correct calculation of the incurred costs within the company. CONCLUSIONS AND FURTHER WORK Maintenance decisions are seldom based upon the change in a single parameter, but by analysing the relafionships between different parameters. Traditional CMM systems often rely on user experience during the analysis of these trends but this can be a difficult task due to the numerous techniques and systems available. EXAKT is one system that has been developed to aid the process of analysis. This case study showed that the combination of key parameters within a statistical model could aid the reduction of maintenance costs within a typical engineering company. The model enabled a possible saving of approximately 15% in the maintenance cost for that particular machine and 917
event type. The choice of which parameters to use is dependent not only upon the successful identification of failure modes, but also the failures observed during the plant's working life. Detection of novel situations within the working life of plant items needs further work. There are numerous tools available for this process, for example Principal Component Analysis, which clusters together groups of measurements relating to defined situations so that any novel situations will result in a new cluster. These can subsequently be included in the model and further decrease disruption of production capability. The EXAKT"^^ system makes extensive use of statistical methods in its modelling and subsequent decision process within the level 1 data fusion framework. However, a number of areas were highlighted where intelligent systems could be better used to aid the development of this type of system. These areas include problems with missing values in the initial data, novelty detection within parameter association and the diagnostics of the actual problem. REFERENCES 1. Andersson C & Witfelt C. 2000, ''Advisor:- A prolog implementation of an automated neural network for diagnosis of rotating machinery ", http://www.visualprolog.com/vip/articles/CarstenAndersonyadvisor.html 2. CBM Consortium, 2000, "EXAKT^^ - The CBMOptimiser", http://www.mie.utoronto.ca/labs/cbm 3. Design Maintenance Systems (DMSI) 2000, "MAINTelligence monitor ", http://wvsrw.desmaint.com 4. Hall D.L. and Garga A. K., 1999, "Pitfalls in data fusion (and how to avoid them) ", Key note speech, EuroFusion99, ISBN 0 9537132 0 2 5. Hall D.L., and Llinas J, 1998, "An introduction to multi-sensor data fusion", Proceedings of the IEEE international Symposium on circuits and systems. Vol 6, pp537-540 6. Harris T.J and Kirkham C , 1997, ''A Hybrid Neural Network System for Generic Bearing Fault Detection" , Proceedings of COMADEM 97, Vol 2, pp 85-93, ISBN 951-38-4563-X 7. Kennedy I., 1998, "Holistic Condition Monitoring", Wolfson Maintenance, Proceedings 1'^ Seminar on Advances in Maintenance Engineering, University of Manchester 1998 8. Oliver Group, 2000, http://oliver-group.com/ 9. Smith G.T, 1996, "Condition Monitoring of Machine Tools ", Southampton Institute, Handbook of Condition Monitoring, Rao (Ed.) 1'' edition, pp 171-207, ISBN 185617 234 1 10. Starr A, Esteban J, Hannah P, Willetts R, Bryanston-Cross P, 2000, "Strategies in Data Fusion for Condition Monitoring", Proc 3rd Int. Conf Quality, Reliability, Maintenance, Oxford (invited) 11. Taylor 0. and Maclntyre J., (1998), "Modified Kohonen Network for Data Fusion and Novelty Detection Within Condition Monitoring", Proceedings EuroFusion98, pp 145-155 12. Trave-Massuyes L., Milne R., 1997 ''Gas-turbine condition monitoring using qualitative model-based diagnosis", IEEE Expert, May/June 1997, pp22-31
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DIAGNOSTICS OF HONEYCOMB CORE SANDWICH PANELS THROUGH MODAL ANALYSIS R. Basso', C. Cattaruzzo^ N. Maggi^ and M. Pinaffo' 'Dipartimento di Ingegneria Meccanica, Universita di Padova, Italy ^Officine Aeronavali, Tessera-Venice, Italy
ABSTRACT The possibility of using a new diagnostic technique to identify the presence of localised debonding in metaUic sandwich panels with honeycomb cores is analysed in this paper. The technique is based on the natural frequencies measurement of panels excited with an impulsive load. A preliminary simulation demonstrated that when there is a debonding it is possible to identify peaks relative to the vibrations of the debonded skin seen as a plate clamped at boundary. The tests on panels with artificial debonding, using an accelerometer and a high sensitivity displacement laser sensor, validated the simulation. The no-contact displacement sensor is suitable for its capability to pick up the vibrations of the skin when placed above the debonded area, but its frequency field is limited as it can only identify defects larger than about 60-70 mm. On the contrary the accelerometer reveals the debonded areas if it is placed outside them. KEYWORDS Sandwich structures, Modal Analysis, Debonding detection, Honeycomb structures. Vibration measurement.
INTRODUCTION Many parts of aircrafls (flaps, wings, ailerons, elevators and rudder) are made up of sandwich panels with an aluminium alloy honeycomb core. Even though in the past few years non-metallic composite materials have been used more and more, these structures are still used because of their great flexibility, high resistance, low density and isolation properties to noise. These panels sometimes have areas where the skin and core have become debonded, due to the vibrations caused upon impact as well as due to moisture infiltration or defects in the construction made during the assembly. During revision, these areas are identified using ultrasonic probes. The damaged area is subsequently repaired using a procedure in which the localised skin is removed, the core substituted, if it is damaged, and a new skin bonded back on. Ultrasonic technique to diagnose damaged areas takes a significant amount of time because the surface to be examined is so large and because the surface has to first be carefully cleaned using solvents and then covered with a gel which has to be removed at the end of the revision.
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Debonded areas having a diameter less than 30 mm are particularly difficult to identify. Furthermore, in the sandwich structure the opposite side of the ultrasonic probe is difficult to examine because of the significant transmission loss through the honeycomb structure. The aim of this paper is to evaluate the possibility of using a complementary diagnostic technique based on the dynamic response of the panels when they undergo an impulsive excitation. In fact, it is well known that structural damage leads to variations in the modal parameters and by measuring these it is possible, in many cases, to identify and localise the damaged area. In this work, also, we would to compare two vibration transducers, an accelerometer and a no-contact motion sensor. The technique proposed in this paper is fairly simple since it is based on the comparison between the frequencies of an undamaged panel and those of the damaged one. It is important to point out, however, that modal analysis should be considered complementary to the ultrasonic technique and used to speed up the process of identifying debonded areas. The analysis of frequencies could be considered to be a prehminary indicator of the presence of possible debonding. Then the final analysis would nonetheless be carried out using the ultrasonic technique since it provides greater precision regarding the position and extent of the debonding. To evaluate the feasibility of this technique, a preliminary numerical analysis using finite elements was carried out followed by experimental modal analysis tests on four sandwich panels, one of which was undamaged while the other three had areas which had been artificially damaged.
MODAL ANALYSIS WITH FEM In order to evaluate the possibility of using a diagnostic technique based on the modal analysis of sandwich structures with a honeycomb core, a numerical study using finite elements was carried out on four panels built for the purpose of both the numerical study and for the following experimental analysis. One of the panels was undamaged while the other three had circular "debonding" with a radius of 75 mm created artificially in different positions by not applying the 0.2 mm-thick adhesive. It is important to note that simulated defects may be slightly different than real ones. In fact, in the simulated defect used here between the skin and the core there was no adhesive, while in the case of a real debonding, the adhesive remains attached either to the skin or the core. Therefore, in the simulated debonding, the skin was free to oscillate and behaves like a fixed circular plate, while in the case of real debonding, the skin may be not completely free to oscillate. The total dimensions of the panels studied were 500x500x25.6 mm. The skins were made up of two 0.8 mm thick sheets in a aluminium alloy Al-Zn, 7075, hardened and aged to the T6 state. These layers can be considered isotropic and, because of their characteristics, three elastic parameters were sufficient. The core was made up of NIDRATAN® honeycomb in alloy Al-Mg, 5056. The basic cell is a regular hexagon with 6 mm between any two sides. The cell walls are 0.1 mm thick and 24 mm high. The honeycomb core can be considered orthotropic, i.e. having three symmetrical planes, and therefore 9 independent elastic parameters were needed to describe its behaviour. These parameters were calculated based on the constants of the constitutive alloy. The x-y plane constants were calculated as suggested in Masters & Evans (1996). The elastic parameters (Ez, Gxz, Gzy, Vxz and Vzy) of the x-z and y-z planes were calculated using ANSYS 5.5.1 to carry out FEM simulations since data suitable for calculating them cannot be found in the literature. To make these calculations, traction and shear tests were carried out on the basic cell of the honeycomb and consequently the parameters were defined. In Table 1 the mechanical characteristics of the two alloys of the base and of the honeycomb can be seen. The model of the undamaged panel was made using 400 SHELL91 elements of the ANSYS code. The SHELL91 elements were conceived specifically for modelling sandwich structures (each element has 8 nodes and 6 degrees of freedom for each node). The model of the elements is further defined by the thickness of the various layers and the direction of the fibres if composite materials and support materials with an orthotropic behaviour are used. The model of the elements uses the "sandwich logic" which was specifically designed for modelling sandwich structures which have two thin layers and a
920
fairly thick core. It can be assumed that the core supports the entire shear load and that the skins support the bending load. TABLE 1 BASIC ALLOYS AND HONEYCOMB MECHANICAL CHARACTERISTICS BASIC ALLOYS
Density (kg/m"^)
Al-Zn 7075 2798
Young's modulus (MPa)
71000
Shear modulus (MPa)
26692
Poisson ratio Breaking stress (MPa) Yield stress (MPa)
0.33 480 420
HONEYCOMB
Al-Mg 5056 118.2 2660 Ex = Ev = 0.173 74000 Ez= 10083 G,v = 0,043 27820 G^ = 892.9 Gvz = 343 0.33 Vxy = Vxz = Vxz = 280 130
0.33
The model of the panel with the debonding was based on the one presented in Liew et al. (1997), where a typology of a defect analogous to the one being studied is presented. The debonding between the skin and the core was set up as an empty space having the same thickness as the missing adhesive. The simulated constraints were free body constraints obtained by coimecting the panels to four fixed points using CoMBlNl4 elements which were assigned a stiffness of 20 N/m. A description of the model used is presented in more detail in Basso et al. (2001). Figure 1 shows same patterns of the finite element model.
Fig. 1: Model of panels. A Undamaged panel. B A quarter of damaged panel. C Detail of the empty volume at the centre of the damaged panel. The Block Lanczos model, which is based on the Lanczos algorithm, was used to solve the eigenvalue problem since it is faster than other methods. The naturalfrequencieswere held in consideration more than the other results obtained from the numerical simulation to evaluate the feasibihty of the method. Nonetheless, the modal shapes, and the relative nodal lines, were useful in estabUshing the measurement points for the experimental tests. The results of the numerical simulation in the panel with the debonding, compared to the undamaged one, show new frequencies, and therefore the defect created new vibrating modes. Furthermore, it is worth noting that the natural frequencies of the panel did not undergo significant variations. The new ways of vibrating refer only to the circular area of the skin above the unbonded area. In fact, this area has a different stiffiiess than the one in the undamaged panel as the skin and core are independent of one another (there is no adhesive to connect them) and therefore the debonded skin behaves like a plate clamped at boundary. 921
EXPERIMENT The experiment to verify the feasibihty of the proposed diagnostic technique was carried out on 4 sandwich panels: one was integral and three each had an artificial circular debonding, 150 mm in diameter. In the 3 panels with a defect, the debonding was carried out respectively: in the centre, in the middle near one side, in a comer. When the ultrasonic technique was used to verify the panels, only the central artificial debonding presented a total absence of glue between the skin and the honeycomb, while the other two presented traces of glue between the skin and the core even though the skin was completely detached fi-om the honeycomb. Though the traces of glue were not used intentionally, their presence actually made the latter two debondings more similar to real debonding. The panels were excited using an impulse force hammer and the natural fi-equencies were read using both a piezoelectric accelerometer (PCB type M352C65) and a highly sensitive laser displacement sensor (MEL type M5/2). This latter instrument was used in such a way that the natural frequencies of its support did not interfere with the ones of the panel being tested. A dynamic signal analyser SIGLAB was used for signal processing and fi'equency spectra visuahsation.
Fig. 2: Test rig: accelerometric (left) and displacement (right) measurements. Only the natural fi-equencies were studied so as to avoid a complete modal analysis by observing the modal shapes as well. This was done for two reasons: first of all because carrying out a complete modal analysis would require a lot of fime and, just for this, secondly because it would not be worth using it as a diagnostic technique complementary to the ultrasonic technique. The fi-equency ranges was limited to 5 kHz for accelerometric measurement and to 2 kHz for displacement one. This last is less than the first because the motion signals produced beyond that limit are too weak to be picked up by the used laser sensor. The two sensors were positioned in different places, both above the debonded area and away fi-om it. The hammer hit the panel in various points, but in particular it hit the panels along the borders. All of the frequency spectra obtained are the result of an average of several frequency spectra obtained by keeping fixed both the point of measurement and the point of impact of the hammer.
RESULTS In Figures 3 and 4, some examples of the results of the numerous experimental tests can be seen. The spectra of the panels with localised debonding, in addition to presenting peaks for the same frequencies as the undamaged panel, show new peaks due to the extra degrees of freedom in the structure corresponding to the debonded area. These new peaks correspond to the frequencies of the parts of the debonded circular areas. This was verified calculating the natural frequencies of a circular plate clamped at boundary.
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Fig. 3: Examples of acceleration spectra. The accelerometer was placed outside the debonding. A Undamaged panel. B Central debonding. C Debonding near the middle of one side. D Debonding in a comer. A
L»>*^i»^VMwW ' ^ N N A ' N ' ^ . A ^ \ A J K ^ A » V N ^ ^
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*-,
fy'^^AyVyj WfvW1ft/yA>u^^^
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1000 Hertz
1500
|W>^^vMVvV W ^ j t M ^ X i A ^ - ^ ' ^ ^ ^ 1 U A > 4 U ^ JX M ^M A /V MW^^TVWU,,,^^
1000 Hertz
Fig. 4: Examples of displacement spectra. The laser sensor was placed above the debonding. A Undamaged panel. B Central debonding. C Debonding near the middle of one side. D Debonding in a comer. The spectra depend on the position between the sensor and the debonding, while they seem to be indifferent to the position between the sensor and the impact point. The accelerometer is better able to 923
identify the new peaks if they are positioned outside of the debonded area, while the displacement sensor is able to identify the new peaks caused by the debonding only if the beam is directly above an debonded area. In the latter case, the limit of the frequencies which can be read is about 2 kHz, and, considering the results of the simulation with fixed circular plates, it can be deduced that with the laser sensor it is possible to see debondings with a diameter greater than ca. 60-70 mm. Even though the accelerometer is able to read free vibrations of the debonded skin even when the it is far from the defect, it is difficult to interpret the signals it provides with respect to those provided by the laser displacement sensor at the edges of the debonded area, hi order to overcome this limitation, a technique was studied to make it possible to identify the debonded area. With this technique it is possible to read the spectra of the signal of the accelerometer in three different points of the panel, having continuously applied the impulsive force in the same point. By comparing the spectra obtained with this method to those relative to the undamaged panel, it is possible to find the debonded area.
CONCLUSIONS This paper analysed the possibility of using a new diagnostic technique to identify the presence of localised debonding in metallic sandwich panels with honeycomb cores. This technique is based on reading the natural frequencies of panels excited with an impulsive load. A preliminary FEM simulation revealed that when there is a debonding, in addition to the having the same peaks as the undamaged panel, it is possible to identify peaks relative to the vibrations of the debonded skin seen as plate clamped at boundary. The same peaks which were seen in the simulation were also identified as vibrations in the experiment using an accelerometer or a high sensitivity displacement laser sensor. The no-contact displacement sensor is, however, the preferable of the two since it is able to pick up the vibrations of the skin when placed above the debonded area. On the other hand, the frequency field of the displacement sensor is limited as it can only identify defects, which are larger than ca. 60-70 mm. The limitation of the accelerometer is that it requires more comphcated measurement procedures, but if in the future an automatic technique is set up, it should be possible to perform an entire component inspection by a single impulsive excitation and, therefore, in short time. The diagnostic technique studied could help reduce the time needed to carry out the ultrasonic technique used today. In fact, the laser technique if applied with many sensors placed over the more critical areas of the component may perform in short time a rough inspection of the component. The accelerometer technique may be applied to locate many defects, placed everywhere inside the component, or at least to exclude the presence of defects, by a single impulsive load application. Moreover, for both techniques it is important to highlight the possibility to perform the inspection without removing the component from the aircraft and without stripping it from paint or sealant.
References Basso R., Cattamzzo C , Maggi N., Pinaffo M. (2001). Modal analysis of sandwich panels with localized debonding. (in Italian), 16* AIDAA Conference, 24-24 Sept. 2001, Palermo, Italy Liew K.M., Lim C.S. (1997). Analysis of dynamic responses of delaminated honeycomb panels. Composite Structures 39:1 -2, 111 -121. Masters I.G., Evans K. E. (1996). Models for the elastic deformation of honeycombs. Composite Structures 35:4 403-422. Paolozzi A., Peroni I. (1998). Identification of models of a composite plate for the purpose of damage detecfion. Aeronautica, Missili e Spazio 67:3-4, 119-129 Rao J.S. (1999), Dynamics of plates. Marcel Dekker, Inc. Scott Burton W., Noor A.K. (1997). Structural analysis of the adhesive bond in a honeycomb core sandwich panel. Finite Elements in Analysis and Design 26, 213-227. Soedel W. (1993). Vibration of Shells and Plates, Marcel Dekker, Inc.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
ASSESSMENT OF STRUCTURAL INTEGRITY MONITORING SYSTEMS B. de Leeuw ^ and F.P. Brennan ^ 'UCLNDE Centre Department of Mechanical Engineering, University College London Torrington Place London WC1E7JE England
ABSTRACT Recent advances in electronics and remote communication technology have led to new activity in the area of structural integrity monitoring. A new discipline of designing and specifying the requirements of structural monitoring systems is emerging. In order to be effective, the design of these new systems must be led by an understanding of the requirements of maintenance and structural integrity calculations. In addition, monitoring functions need to be characterised and objectively evaluated. Measurement of performance characteristics needs to be established so that different equipment can be objectively compared. Crack detection and sizing systems for example, can be evaluated in terms of POD (Probability of Detection) and POS (Probability of Sizing). Similarly stress/strain-monitoring devices need to be evaluated in terms of accuracy of measurement with respect to material, environment, static and dynamic loading parameters, repeatability and drift. In addition, stress/strain-monitoring systems are likely to respond differently to different loading regimes. For example, some systems will respond well to low R-ratio cyclic stresses but may not perform as well under high mean stress or vice-versa. Similarly the frequency of applied cyclic stresses will also effect performance of monitoring systems. This paper presents details of a recent laboratory study of two Stress Monitoring systems. The two systems, ACSM (Alternating Current Stress Measurement) and the Stress Memory Unit were subjected to performance reliability trials and results presented in a manner which allow comparison with other systems. The aim of the study was to investigate the basis for performance reliability trials of stress/strain-monitoring systems and ways in which data can be presented. In the future, this type of data will be integrated into structural reliability calculations and may be used to assist conventional inspection scheduling.
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KEYWORDS Structural Integrity Monitoring, ACSM, stress monitoring, assessment
INTRODUCTION The safety and integrity of engineering structures has always been a primary concern for operators and regulatory authorities. Un-noted flaws in structures could result in catastrophic failure, which would not only be very costly in monetary terms but could also lead to loss of life. To avoid such an event it is important to be able to determine the location and extent of any structural damage so that appropriate action can be taken to ensure structures and components are fit-for-purpose. Currently the general procedure to ensure the integrity of large structures such as offshore platforms consists of applying a number of established non-destructive testing and evaluation techniques on selected members. The inspections are carried out by a number of specialised resources such a deep sea-divers and remotely operated vehicles (ROV). This usually follows a planned schedule that could span over a period of months and tends to be a costly operation as well as potentially dangerous for the people directly involved. Although this form of inspection has proved to be useful in reducing the number of catastrophic failures in many types of engineering structures it still has a number of shortcomings. The most important is the fact that inspections are only carried out at predefined scheduled times. Rare or extreme events may cause flaws in an engineering structure which are unlikely to be detected until the next scheduled inspection, which could be too late. Such a situation arose during the Northridge earthquake (California, 1994), several structures were weakened (but undetected) following the main shock, however they only collapsed when a major aftershock occurred [1]. Recent developments in electronic sensors and data acquisition and transmission systems and their reducing cost has led to the emergence of a new branch of non-destructive evaluation known as 'Structural Integrity Monitoring' (SIM). SIM can be defined as continuous monitoring of engineering structures, where the aim is to record data relating to the integrity of the structure with the view to comparing it to a previously set trend based on the historical behaviour of the structure. Any major differences observed between the data should be investigated further as it could indicate the presence of a flaw in the structure. New state of the art in Structural Integrity Monitoring is very different to some of the existent monitoring techniques such as vibration monitoring. Massive improvements in sensing technologies, data acquisition and storage systems and GSM communication systems have greatly expanded the possibilities available for SIM. It is possible for example to distribute a number of wireless sensors over a structure and have the data transmitted to a central receiving point where the data can be processed. Data can be analysed over a monthly period for example or for some applications it may be necessary to analyse it in real time. One of the main aims of SIM is to store as much historical data about the structure as possible to enable future comparisons to be made, and to give indications on the behaviour of the structure during rare or extreme behaviour such as storms or even collisions. The new state of the art SIM techniques under development result in some important direct benefits for end users, maintenance crews and manufacturers of engineering structures. It allows for safe inspections to be carried out in areas of difficult access or that are potentially dangerous. The storage of historical data potentially closes the design loop [2], providing manufacturers with invaluable historical data indicating the performance of their structure in service. SIM minimises the level of human involvement required, reducing downtime and human errors [1]. Automation in general will improve safety and reliability. Continuous monitoring may also potentially affect the working life of a structure in a less obvious way. Currently the design life of a structure is determined in advance by considering its design and typical experiences. This may not be very accurate as it does not take into account 926
individual structures throughout its working Ufe with respect to the degree and conditions of usage. This is Ukely to vary from one structure to another and thus similar structures could have very different operational lives. SIM would change this, as by monitoring continuously the relevant maintenance can be carried out when necessary and decommissioning would only take place once this is absolutely necessary. Structural Integrity Monitoring not only promises to eliminate the uncertainty associated with inspection scheduling but will, in addition, allow for new advanced structural design philosophies that were not previously possible. An example of this is the "Controlled Failure Design" concept being researched at the UCL NDE Centre [3]. This predetermines the least inconvenient failure mechanism at the design stage. Structural Monitoring would allow engineers to monitor and verify the progressive failure process without affecting safety. Structural Integrity Monitoring is an emerging technology, much research still has to be carried out in order to assess the applications available and to determine if they are capable of performing their tasks with an acceptable level of reliability, sensitivity, accuracy and cost effectiveness [4]. This paper presents preliminary work investigating the operating characteristics of two innovative remote stress-monitoring devices (the Alternating Current Stress Measurement (ACSM) technique and the Material Stress Memory unit).
ASSESSMENT OF SIM SYSTEMS Along with the recent advances in electronics and remote communication systems comes an increased number of innovative SIM techniques. These vary in terms of application and parameters recorded to determine the integrity of a particular structure. The level of performance of each monitoring technique will vary depending on the type of structure being monitored. It is therefore important to be able to evaluate the accuracy of the measurements provided by the different techniques in order to assess their suitability for monitoring the integrity of any particular structure. Factors affecting the monitoring performance of a particular application include construction material, the operating environment, the type of loading experienced by the structure (eg static or dynamic responses), repeatability and drift. One point that should be noted its that no one SIM technique will provide an ideal solution for the monitoring of a structure's integrity. A combination of these should be applied to ensure maximum monitoring efficiency, therefore assessment of combined techniques is also necessary. For effective comparisons between systems it is necessary to design a procedure that will enable a direct and easy assessment of each system under certain predetermined criteria or parameters. These criteria will vary depending on the type of structure the application is to be used for. The SIM techniques can initially be tested within a laboratory environment in order to pre-determine system characteristics. Ideally one would want to end up with single indexed figure indicating the relative performances of SIM technologies for different applications. Currently Crack detection and sizing systems have such performance measures. Here the performance of the applied techniques are established using POD (Probability of Detection) and POS (Probability of Sizing) respectively. However with SIM a greater number of influencing parameters and large volumes of data makes comparison of system characteristics more difficult. When assessing the accuracy of a SIM device there a some factors of particular interest that need to be considered. Theses include Repeatability - The capability of the device to reproduce similar results every time a test is repeated under identical conditions.
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Sensitivity - This the sensitivity of the sensor in terms of its response to various stress levels as well as the sensitivity of the device in recording interferences such as background noise and how these affect the overall data recorded. Drift - This is the extent to which there are whole shifts in the mean of the cyclic data recorded without there being a significant change in the structures condition. These should be considered under different loading conditions and operating environments. Research into the analysis of the data and its presentation in a format that makes direct comparisons between SIM systems possible is still in its preliminary stages. Many of the earlier work is based on statistical inferences however it has become obvious that these are not always adequate when looking at the recorded data distributions. Some of the methods considered for representing SIM data are presented below: Data Distributions - These consist of graphic displays of the frequency distributions of the recorded data. The best performing systems would have a very steep data distribution with very little data dispersion from the mean. It is necessary to devise a procedure that enables the calculation of the maximum permissible deviation from the mean under the various loading conditions. This may be done by applying S-N curves. Mean over time - A useful way of determining the presence of drift in recorded data is to plot the variation of the cumulative mean of Mean Stress and Stress Range of a designated period of time. Figure 1 below illustrates such representation for mean stress, the solid line represents the actual mean stress applied, the dotted lines illustrate the drift in the data recorded using two different SIM techniques.
CO M
55
86 84i
74f 72r
7oir Figure 1: Variation of Mean Stress over 15 min intervals Mean vs Median - When considering the data h may be useful to compare the variation of the mean with the variation of the median of the data with time. Major differences between the two values should indicate a change in the general form and position of the data distribution. This shows that both these parameters should be considered as erroneous conclusions could be made if the statistical mean is only considered as a basis for analysis. Variation of standard deviations - This has the potential of providing useful information regarding the overall spread of the data. However as will be seen later it must be considered along with other assessment parameters as it may provide misleading results under some conditions. The standard deviation can also be used as an indication of the settling period for a particular monitoring device. This is the time it takes between initial operation and its optimal operation. Hence variations in the data after this period could be assumed to have not been caused by the sensing device. This can be seen as 928
the point at which the standard deviation ceases to change over time, hence tending to a horizontal line. The general idea behind this is illustrated in figure 2.
Settling Time
Standard Deviation
Time Figure 2: Variation of Mean Stress over 15 min intervals
PRELIMINARY EXPERIMENTS SIM Techniques utilised Two Stress Monitoring techniques were evaluated during this study. The background to each of these is given below. Alternating Current Stress Measurement (ACSM) ACSM is a non-contact stress measurement technique developed at University College London and is based on the more established 'Alternating Current Field Measurement' (ACFM) method. The basis of both these techniques is that the uniform electric field is induced into the section of a component to be inspected, resulting in a near surface magnetic field due to the 'skin effect ''. The presence of surface breaking flaws will lead to changes in the magnetic flux density, which can be measured and analysed to determine the size of the flaw. This is the principle upon which ACFM operates. ACSM broadens this application to enable the non-destructive and non-contact measurement of stress [5]. Material Stress Memory Unit (MSM) This new Structural Monitoring Technique measures and logs the occurrence of stress/strain cycles. The system used is based on an 120 ohm resistance strain gauge. The advantages of the Stress Memory Unit over conventional stress measurement systems is that it is a miniature, passive and self contained (wireless etc) device designed for use in the field. Outline of Procedure All the experimental investigations were set-up and carried out in the NDE Laboratory at University College London. For operation of the ACSM Probe a U12 Crack Microgauge was required Both of these are manufactured by TSC Inspection Systems Ltd [6]. The probe was simply attached to one of the faces of the test specimen used. The other face was thoroughly polished to allow the attachment of the Strain Gauge, which was used with the Material Stress Memory unit. The test specimen itself was a mild steel plate, 130mm x 50mm xlOmm. The specimen was dynamically loaded using a servohydraulic INSTRON test machine operated under load control.
' 'Skin Effect' - when a high frequency alternating current is applied to a conductor, the current will flow in a thin layer of the conductor 929
Although both these monitoring devices operate with their own separate software packages a virtual data logging application was used to record the experimental data. Voltage outputs from the ACSM Probe, the Material Stress Memory Unit and the load cell were input into the data logger. This enabled ail the data to be recorded simultaneously. Due to the large volume of data involved in these tests the ASCII file format was used to store the data. The test specimen was subjected to a combination of Mean Stress and Stress Ranges ranging between 60 - 110 MPa and 40 - 120 MPa respectively. The cyclic frequency was set at 0.5 Hz and all the tests were run over twenty four hour periods. A full description of the experimental set-up and the date manipulation involved in this investigation can be found in reference [7]. Results The data obtained from the two monitoring devices were normalised with respect to each other in order to enable direct comparisons to be made. Figure 3 below illustrates the two parameters of the stress cycles that were considered during the analysis. AMP
Time
Figure 3: Illustration of measured parameters Hence changes in H and/or AMP indicate relative changes in the average level of mean stress and/or stress range experienced by the test specimen. One of the performance characteristics investigated was the medium term 'Drift' of the data. In order to do, this averages of H and AMP on a fifteen-minute basis were plotted against time for the whole duration of the test periods. The data obtained for both applications was compared directly to the output from the load cell. Sample plots from one of the tests are illustrated in figures 4a and 4b below. a. Variation of Mean AMP over Time
b. Variation of Mean H over Time
Hours
Hours
s
Figure 4: Average Mean Stress recorded over Time It can be seen that for the Stress Amplitude the results for both devices coincide well with the load cell output for the whole duration of the test. When looking at the results for the mean stress it was
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observed that there was some drift in the data recorded from the Material Stress Memory Unit. However in actual terms the equivalent stress variation equated to +1-2 MPa which is negligible. Another basis for comparison was the actual data distributions for the mean stress and stress amplitude ranges for the two devices. Figures 5a and 5b show samples of the data distributions for one of the tests where applied Mean Stress and Stress Ranges were 60 MPa and 120 MPa respectively. a. Distribution of Data for Mean Stress Recorded by ACSM
O
b. Distribution of Data for Mean Stress Recorded by MSM
6000
6000
4000
4000
2000
2000
0
0
3
0 20 40 60 80 100
0 20 40 60 80 100 P4
Mean Stress (MPa)
Mean Stress (MPa)
Figure 5: Sample Data Distributions for Recorded Mean Stress For the measurement of Mean Stress can be seen that the greatest proportion of the data correlates well the load cell output of 60 MPa. Results obtained for measurements of the stress range were on the whole also found to be accurate. However a small number of outlying points were observed in the data recorded by the Material Stress Memory Unit. In order to enable more accurate comparisons to be made between data sets of the different experimental tests, changes in the Standard Deviation as a percentage of the Mean Stress (H) and Cycle Amplitudes (AMP) were observed. The results obtained are summarised in figures 6a and 6b below.
Variation of % Standard Deviation from Mean for Cycle Stress 30 Stress Range
25
m 40 MPa
20 15 10
Load Cell
ACSM
0 H^J?='sC?"^^
60 80 110
MSM
Jlltin—unJl a .
60 80 110 60 80 110 Mean Stress (MPa)
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B 60 MPa D 120 MPa
Variation of % Standard Deviation from Mean for Cycle Stress Range Stress Range D 40 MPa • 60 MPa o 120 MPa
60
80
110
60 80 110
60 80 110
Mean Stress (MPa)
Figure 6: Variation of Standard Deviations from Mean values of cycle stress range and cycle mean stress. Overall both monitoring devices were found to accurately measure the cyclic stress experienced by the test specimen. A general observation for both these applications was that their performance did seem to be dependent on the applied cyclic stress range, improving at larger stress ranges. The only sets of results that could be considered unsatisfactory was the data collected using ACSM at the lowest monitored mean stress and cyclic stress range, both at 40 MPa. Closer inspection of the data distributions for these particular tests provides an explanation for this. From these it was observed that the cause of the high estimates for the standard deviations were caused by two small clusters of data located at either side of an otherwise very concentrated distribution. The likely cause of the problem is a low sensitivity of the ACSM sensor making it difficult to accurately take measurements at lower stress ranges [8]. This would increase the effect that any background noise may have on part of the data, which may be represented by the two clusters of data on either side of the mean. These observations did highlight the point that the standard deviation did not always provide an accurate measure of the overall accuracy of the applications and should therefore cannot be used as a stand alone measure of the accuracy of a structural integrity monitoring device. The results obtained using the Material Stress Memory Unit were found to be comparable, if not more accurate, than those obtained using the ACSM probe. At this early stage of development of both these structural monitoring techniques the results are very promising, especially bearing in mind the ACSM system was not designed for dynamic stress monitoring but for static stress measurements only. The simplicity of application for both these applications and their accuracy compared to other structural monitoring techniques in existence potentially make these very attractive tools for this type of inspection.
CONCLUSION New state of the art Structural Integrity Monitoring applications yield a number of important benefits to the various parties involved in structural engineering in terms of reduction of costs, increased safety and in the possibility of closing the design loops. However as more SIM systems become available it is becoming increasingly important to develop a procedure for assessing these against each other in terms of suitability for particular applications. Much of this work will entail devising ways of analysing and representing the vast amounts of data recorded by SIM systems. It was shown that care must be taken 932
when using simple statistical parameters such as standard deviations and means as measures of performance, and that these should always be used in conjunction with other measures. From the experimental investigations it can be concluded that the two monitoring systems investigated showed great potential as stress monitoring devices. This is bearing in mind that both these systems are in their design stages and one of them, ACSM, was not originally design for dynamic stress measurement. Also the simplicity of their practical application compared to other structural monitoring techniques make them attractive tools for this type of inspection. REFERENCES [1] [2] [3]
[4] [5] [6] [7] [8]
Chang F. (1997). Structural Health Monitoring: A Summary Report on the First Stanford Workshop on Structural Health Monitoring, Stanford University. Moss R.M. and Matthews S.L. (1995). The Structural Engineer. In-service structural monitoring: A state-of-the-art review 73:13,214-217. Dover W.D., Brennan F.P. and Etube L.S. (2000) Proceedings of the Fifth International Conference on Engineering Structural Integrity Assessment. Structural Integrity Monitoring using Alternating Current Field Measurements. 307-316 Health & Safety Executive. (1998). Progress in Structural Monitoring. Offshore Technology Report-OTO 98 046 Chen K., Brennan F.P. and Dover W.D. (2000). NDT&E International. Thin-skin AC field in anisotropic rectangular bar andACPD stress measurement 33, 317 - 323 TSC Inspection Systems Ltd, 6 Mill Square, Featherstone Road, Wolverton Mill, Milton Keynes MK12 5RB De Leeuw B. (2001). MSc Thesis. Assessment of a Structural Integrity Monitoring Device. Dept. of Mechanical Engineering, University College London Dover W.D., Brennan F.P. and De Leeuw B. (2001). Proceedings of OMAE 2001: 20th International Conference on Offshore Mechanics and Arctic Engineering. ACSMStressprobe: a new non-contacting stress measurement technique for the offshore industry
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
EXPERIMENTAL VALIDATION OF THE CONSTANT LEVEL METHOD FOR IDENTIFICATION OF NONLINEAR MULTI DEGREE OF FREEDOM SYSTEMS G. Dimitriadis School of Engineering, The University of Manchester Oxford Road, Manchester Ml3 9PL
Abstract System identification methods for nonlinear dynamical systems could find uses in many applications such as condition monitoring, finite element model validation and stability determination. The effectiveness of existing nonlinear system identification techniques is limited by various factors such as the complexity of the system under investigation and the type of nonlinearities present. In this work, the Constant Level Identification approach, which can identify multi degree-of-freedom systems featuring any type of nonlinear function, including discontinuous functions, is validated experimentally. The method is shown to yield accurate identifications of an experimental dynamical system featuring two types of stiffness nonlinearity. The full equations of motion are accurately extracted, even in the presence of a discontinuous nonlinearity. Keywords Nonlinear systems, system identification, multi degree-of-freedom systems, bilinear stiffness, cubic stiffness. Condition Monitoring. Introduction There already exist methods like the NARMAX model (Billings & Tsang 1989), higher order spectra (Simon & Tomlinson 1984) and the restoring force method (Crawley & Aubert 1986) which can identify aeroelastic systems given the inputs and outputs. However, these methods have still not reached the level of maturity necessary to allow their application to general aeroelastic systems. Both NARMAX and the higher order spectra method are incapable of identifying systems with discontinuous nonlinearities, such as bilinear stiffness or freeplay, which are common in aeroelastic systems. The restoring force method does not share this limitation, but its application to multi degree of freedom systems is still problematic.
935
A further consideration that must be made is whether the identification process is parametric. The analysis of an identified system is much simpler when the terms in the model resulting fi*om the identification process are parametric, i.e. model explicitly the non-linearities present in the system. However, both NARMAX and the restoring force method yield better results when using non-parametric as well as parametric terms. Hence the resulting model contains terms without any physical meaning. In Dimitriadis & Cooper (1998) the Constant Level Identification (CLI) approach for the identification of nonlinear dynamical systems was presented. The method is a development of the Restoring Force technique, adapted to identify any nonlinear function(s) present in one of the system states without the need for curve fitting. The method is flexible enough to be able to deal with a large class of nonlinear systems and functions. For example, in Cooper & Dimitriadis (1997), a demonstration of the use of the CLI approach in conjunction with gust load prediction methods for aeroelastic systems can be foimd. Until now, the method had only been demonstrated on simulated systems. In this paper, CLI is employed to identify two experimental systems with two different nonlinear functions, namely cubic and freeplay stiffness. First, the mathematical basis of the method will be briefly explained and, subsequently, the experimental results will be presented. Mathematical basis of CLI Dynamical systems are usually described by the general equation Mq + Cq + Kq = F(0
(1)
where M, C and K are the mass, damping and stiffhess matrices respectively, q is the displacement vector and F is the excitation force vector. With the CLI method, use is made of the fact that, if the nonlinear function depends on only one of the state variables then, at an arbitrary response level, the restoring force due to the non-linearity is constant. The approach estimates the exact equation of motion of the system by curve-fitting the response at this chosen response level. The first crucial aspect of the CLI method is to multiply the equations of motion throughout by the inverse of the mass matrix, so that the mass matrix is no longer a quantity that needs to be identified. The Restoring Force equation becomes q + M-'f(x) = M-'F
(2)
where x is the state vector, given by x = [q q]^ and now M'^F must also be treated as an unknown. The crucial assumption behind the CLI method is that the restoring force function f(x) has linear components and that the nonlinear components depend on only one of the state variables, say the ith component, xt. Then, f(x) can be re-written as f(x) = L[x,
•• x,_,
x,^,
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•••
x^J+g(x,)
where m is the number of modes (or degrees of freedom) of the system, L is a constant matrix coefficient of size 2mx(2m-l) and g(x/) is a 2mx\ vector of linear and/or nonlinear functions which depend only on Xi. Then, equation 2 becomes ^2j'+M-^g(x,) = M-^F
q + M *L[XI
(3)
Figure 1: Picture of experimental setup keeping in mind that this equation, and hence the CLI method, only applies to systems where the nonlinearity really is a function of only one state variable. Finally, M"^F(0 is written as M"* Aw(0 where A is a 2mxl vector of constant amplitudes, w(0 is the 2/wxl vector of the measured inputs to the system and F(0=Aw(/). Hence, the governing equation of the CLI approach is obtained as q + M-'L[x,
••• x._, X,,, ••• X2„,f+M-'g(x,) = M-'Aw(0
(4)
Given measurements of q, q, q and w at times tj where x, is a constant, equation 4 can be expanded in order to solve for the unknown constants L = M"*L, N=M'^g(A:/) and A = M"'A. Expanding for all times tj, equation 4 becomes
^x,a,) • x,{t^)
•
^M(A)
^/+I('I)
•
• ^2»(',)
Mti)
• •
*,-l('2)
^/.iCl)
•
• *2»('2) Mh)
i"!
[ 4,, '
1 1 ^k,lm-\
.^,(^,) •
•
X,-l('«l)
^/+l(f|)
•
• *2»,(^») M'n)
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ij
4
.=.
•^*(',)1 ^^ + 5.7876xl0V2^ + 2.2553xl0V0-1067
(8)
The terms in equation 8 are of order: y2^: 0(1), y2^: 0(10'*), y2^: 0(10), y2^: 0(10"^). Hence, the 0th and 2nd order terms can be neglected and, since the 1st order term is due to the linear cantilever springs, the cubic stiffness caused by the steel ruler is given by Arcubic=l-3xl0V2^- The equations of motion can be written as (after substituting for term A^, 2 the slope of equation 11 and for ^2.2 ^^^ 1st order coefficient of equation 12).
"1 0]J>^'1 i
5.5302
r + -0.4023
[0 ijUJ
-0.4777 4.1891
I;;}
+ 10'
2.2825
-0.3081
-0.3093
2.2555
H-
0
1.5603H',(/)
30x10^2
2.7529^2(0 (9) '
This equation w^as verified by calculating its response to the same excitation signals that were used on the experimental system, see figure 3 which compares the identified and actual accelerations. Identification of system with bilinear stiffness A freeplay spring was attached to mass 2 in the form of a steel ring moving between two restraining pegs at a distance S apart. Mass 2 was still supported on the cantilever plates hence the resulting spring was bilinear. The CLI method was applied to randomly forced response data from the experimental rig. The distance between the two pegs was set to ^ 1.05mm. Figure 4 shows the identified nonlinear term, N2(y2), variation, plotted against the displacement of mass 2. The identified matrices were
0
0.5
1
1.5
h xlO"' Figure 4: A^2(y2) term for system with freeplay stiffiiess, ^ 1.05mm
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2.7471
-0.6596"
-0.6093
3.8485
K = 10'
" 1.1006
0"
-0.2795
0
A=
0.3486 0.3303
The bilinear stiffness only affects N(y2)- The plot of ^(^2) against jV2 was linear, as in the cubic stiffness example. Its slope was K^^ =-0.3121x10^^. The region between the two dashed lines in figure 4 will be called the inner region, the other two regions being called the left and right outer regions. The width of the inner region is 1.05mm, i.e. equal to ±l,000|.is. The latter strain value is usually greater than a large structure such as a bridge is subjected to over its normal service life. The strain resolution of the system at full bandwidth (>10ms) is about ±5)18.
CONVENTIONAL FOIL GAUGE SYSTEM The conventional foil gauge measuring system was designed to accommodate a maximum of 12 resistance strain and temperature sensors for the bridge trials configured as 5 single-axis measurement points and 1 rosette. Thermocouples were also included for temperature measurement at four different locations on the two sections of the bridge. All the strain
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gauges were connected in a full bridge arrangement. The system was specified to measure strain in the range of Ijie to 1000^8 to about Ijie resolution.
LABORATORY TESTS Prior to field deployment, a series of static and dynamic test was carried out in the lab throughout the design and development stage of the instrument to evaluate the performance of the system. Time-varying strain measurements are of interest in large structures such as the field-trial bridge, which was estimated to have a natural frequency of ~5Hz. To this end the dynamic performance of the system was analysed. In the dynamic train testing, a fibre with a single Brigg sensor was bonded to a Im long steel cantilever beam. A resistive strain gauge was also attached to the steel very close to the FBG sensor. The beam was fastened to a mechanical clamp at one end while the other end was free for loading or vibration. Figure 2a and figure 2b below show results obtained with both FBG and resistive strain gauges when the cantilever beam was forced into a lOHz damped vibration. During these tests the optical system was set to a bandwidth of 140Hz. Although the strain gauge readings were not calibrated strain values at this time, it is clearly seen that the two sets of readings follow closely. Static loading tests were also carried out on a similar cantilever beam, in this test
\i
0.5
-
A A
J\
\
""
0.0
hI
S" o -05 o>
1
Iy Vy y y Time (s)
Time (s)
Figure 2b: Dynamic strain readings, foil gauge
Figure 2a: Dynamic strain readings, FBG sensor
- '
-lOO.Op
at
I
-ISO*
I 50
100
150
Time (arbitrary units)
Time(s)
Figure 3a: Stepped loading and unloading strain. FBG sensor
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Figure 3b: Stepped loading and unloading strain. Resistive gauge
however, the resistive gauges v^ere calibrated to read strain and the results form a Bragg sensor and a corresponding resistive gauge when loading the beam incrementally are shown in the figure 3a and figure 3b below. Results such as these were used in sensor calibration and evaluation of sensor attachment technique for strain transfer efficiency. Various bonding substances and techniques were tested and a successful technique of bonding was developed in the laboratory with a constant and near 100% strain transfer.
BRIDGE TESTING Two sets of field trials were planned in the course of the project. Initially a small scaleloading tist was performed at the time of the installation of the two sensor systems with a further major field trials planned for a later period. In this paper results from the preliminary loading tests only are presented. A SOT truck was used during these preliminary tests. Three static loading positions were identified and each loading condition was repeated three times for data comparison and measurement repeatability verification. Dynamic tests were also carried out with the truck driven at 30Km/hr over the length of the bridge. Although data was collected for all the test conditions and various loading positions, a sample of data set is presented here, as a representative of all the tests carried out. During all the tests, the optical fiber sensor system and the conventional resistive gages were run simultaneously to compare results.
STATIC LOADING The axle of the truck was positioned on the sensor points at the M-section (50m away from each support). The bridge was unloaded between each test to obtain a zero reference and load driven to the test position then stopped there for about ten seconds while measurements were taken as shown in figures 4a and 4b.
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
Time (s)
Time (s)
Figure 4a: Static loading strain, FBG sensor
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Figure 4b: Static loading strain, foil gauge
DYNAMIC LOADING For dynamic tests, continuous measurements were taken while the truck was being driven from off-the-bridge on to-the-bridge and off again at 30Km/hr. Figure 5a and Figure 5b show results from such a test. The strain was seen to peak when the truck reached the sensing points (in this case at the M section sensors). The compressive and tensile strains recorded depict the behaviour of the bridge as it was being loaded over various points on and between the six supports.
«
40.0
2 CO
20.0
1 *°|c
0.0 |-
-20.0
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
20
Time (s)
40
60
100 r
f
-
80 [
• O
40 \
T
• 0
9
•
FBG1 Strain FBG2 Strain Resistive Strain
•
f
-
100
Figure 5b: Dynamic strain, Resistive gauge
Figure 5a: Dynamic strain, FBG sensor
60
80
Time (s)
* • 1
• 1
4
6
Sensor position
Figure 6: Strain measured at various points across the bridge for one loading condition - a comparison offibreand foil gauge results
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The results shown in figure 6 represent measurement data taken from 11 of the 32 FBG sensors at various points on the bridge and the corresponding readings taken with the 8 resistive strain gauges during one of the static loading conditions when the SOT truck is parked at the mid-span section (sensor positions 6,7 & 8 on the graph). While higher strain readings were recorded at these points, near the support section (sensor positions I to 5) a small strain was recorded as expected, some of these points were under compressive strain for this particular loading position. Figures 7-9 represent measurement data taken with three axis rosettes from both the FBG system and the conventional resistive strain gauge system at the S section of the bridge for one of the various loading positions tested. The angular orientation of the sensors making up a rosette is'such that 0° represents a sensor aligned along the length of the bridge (maximum strain axis). The data is presented here for comparison purposes only. Note that the data from the FBG system is shown at full bandwidth while the data from resistive gauge system is time averaged.
DISCUSSION For each loading test, both fibre and resistive gauge measurements were taken. The results show a good agreement between the resistive gauges and Bragg grating sensors for both static and dynamic tests. On average measurement results from the Bragg grating sensor system showed a standard deviation of less than 1 p-s, while the resistive gauges showed a maximum standard deviation of 1.8 |LI£. Although provision was made for temperature compensation with a Bragg sensor attached to a strain isolated separate steel as well as conventional thermocouples for measuring local temperatures, no attempt is made here to use such data for compensation as the tests carried out over a short time period (120 seconds maximum), which is too short time for any thermal fluctuations to occur on such a large structure. For long term structural health monitoring however, temperature compensation becomes a concern and as far as this work is concerned, such technique is in place and is due to be implemented in future longer term testing. This work has demonstrated the advances and practicality of using fibre Bragg grating sensors in the instrumentation and monitoring of large civil engineering field structures. It has also shown that when operating in field conditions, FBG sensors can be surface bonded to structures with as much ease as conventional strain gauges elements and thus the superior performance of FBG sensors over foil gauges and in particular multi point sensing for site management can be realised making the FBG system cost effective. ACKNOWLEDGEMENTS This work is part of the EU sponsored BRITE/EURam 'Millennium' Project on the 'Monitoring of Large Civil Engineering Structures for Improved Maintenance'.
949
RESISTIVE GAUGE DATA
BRAGG GRATING DATA
^f0^Wi^ "^ 100
1
w^ -40.0 -20.0
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Figure 7a: Bragg sensor, oriented at 0°
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Figure 8a: Bragg sensor, oriented at 45°
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I
Figure. 7b: Strain gauge oriented at 0°
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_l 15
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Figure 8b: Strain gauge oriented at 45°
*IKV\|fliW
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Figure 9a: Bragg sensor, oriented at 90°
Figure 9b: Strain gauge oriented at 90°
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REFERENCES [1] Kersey A D, Davis M I, Patrick H J, LeBlanc M, Koo K P, Askins C G, Putman M A and Friebele E J. (1997). Fiber Grating Sensors. J. Lightwave Technol. 15 No. 8 1442 -1463. [2] Grattan K T V & Meggitt B T (Eds). (2000), Optical Fiber Sensor Technology, Advanced Applications, Kluwer Academin Publishers. Dordrecht, The Netherlands 79 - 187. [3] Ferdinand P, Ferragu O, Lechien J L, Lescop B, Marty V, Rougeault V S, Pierre G, Renouf C, Jarret B, Kotrotsios G, Neuman V, Depeursings Y, Michel J B, Uffelen M V, Verbandt Y, Voet M R H and Toscano D. (1994). Mine operating accurate stability control with optical fibre sensing and Bragg grating technology. BRITE-EURam STABILOS project Proc.SPIE 2360 162-6. [4] Uttanlchandani D. (1994). Fibre optic sensors and smart structures: Developments and prospects. Electronics and communications journal, 237-246. [5] Liu, T Fernando G F, Zhang L, Bennion I, Rao Y J and Jackson D A. (1997). Simultaneous strain and temperature measurement using a combined fibre grating/extrinsic Fabry-Perot sensor. 12th International conference on optical fibre sensors (OFS). 40-43. [6] Gebremichael Y M, Meggitt B T, Boyle, W J 0 Li W, Grattan K T V, McKinley B, Boswell L. (2001). Practical temperature compensated Bragg grating strain sensor system in smart bridge application: long term structural integrity monitoring. To be published. [7] Haran F M Rew J K & Foote P D. (1998). A strain isolated-Fibre Bragg Grating sensor for temperature compensation of fibre Bragg grating strain sensors. Meas. Sci. Technol. 9 1163-1166. [8] Ning Y N, Meldrum A, Shi W J, Meggitt B T, Palmer A W, Grattan K T V & Li L. (1998). Bragg grating sensing instrument using a tuneable Fabry-Perot filter to detect wavelength variations. Meas. Sci. Technol. 9 599 - 606. [9] Kersey A D, Berkoff T A and Morey W W. (1993). Multiplexed fiber Bragg grating strain-sensor system with fiber Fabry-Perot wavelength filter.Optics Letters 18, No. 16 13701372
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
THE APPLICATION OF OIL DEBRIS MONITORING AND VIBRATION ANALYSIS TO MONITOR WEAR IN SPUR GEARS J.A. Barnes, A.G. StanMaintenance Engineering Research Group. Manchester School of Engineering, Oxford Road, Manchester, Ml3 9PL, UK
ABSTRACT The implementation of a reliable data fusion or intelligent system for the analysis of gearbox vibration and oil debris data has the potential to significantly reduce maintenance costs and to improve fault detection capabilities. An example of this is in the case of faults such as the early stages of gear scuffing where the fault can seem less severe in terms of vibration than it really is. Combining the two technologies would mean an increased perspective of the condition of a gearbox and so enhance the information available for fault diagnosis. It is important to consider that inaccurate sensors will decrease the accuracy of any fusion system. The purpose of this stage of the work is to set up a test rig and monitor the failure of a set of overloaded unhardened spur gears. Once the testing procedures and measured parameters have been refined, this information will be used to investigate the application of a data fusion system to a gearbox. Vibration data, analysis from a portable oil diagnostic system, particle counts from an on-line induction particle counting unit, and microscope analysis data are monitored during the test. These parameters are to be used to chart the wear of the gears through to failure. This paper provides a review of the oil debris monitoring systems and techniques used in the work to date. KEYWORDS Wear Debris Analysis, Tribology, Gears. INTRODUCTION The integration of oil debris analysis and vibration analysis for the detection of gear faults is not a new idea, and has the potential to significantly reduce maintenance costs. A national survey conducted in 1997 put the cost of wear alone on UK industry at around £650 milUon per year (Neale and Gee 2000). If the cost of unnecessary failures were to be added to this then the total would be significantly higher. Work into the data fusion of several data sources would mean earlier fault detection as different technologies can pick up different faults at different stages. This is an advantage of fusing data sources in a fusion architecture or intelligent system as damage detection capabilities can be improved compared with those of a single sensor. Another advantage of this is that quantitative and qualitative 953
information can be combined (Starr et al 2000) so that observational information can be incorporated into the system. In order to put an effective data fusion system into practice for the reliable detection of gearbox faults and minimisation of downtime, this work centres on developing an understanding of how the parameters to be fused relate to each other during the wear process. A gearbox vibrationtesting rig has been modified to accommodate the inclusion of oil debris monitoring. Oil debris monitoring of gearboxes is generally done offline, as samples are taken and analysed in a separate laboratory. Some of the parameters and methods used for testing the condition of oil are listed below: Solid Particle Counting. Viscosity. Moisture. Acid Number. I Colour. Spectroscopy. Ferrography.
TEST MG AND APPARATUS The test rig is designed in a back-to-back arrangement and can accommodate the testing of industrial helical and spur gears. In this case, a pair of unhardened spur gears of facewidth 30mm are being tested. The shaft mountings for the gears are purposefully axially misaligned by 1.5mm and the gears are overloaded by means of a torque plate to 58.86Nm. The pinion and wheel have 30 and 45 teeth respectively and the pinion is driven at 1200rpm. In this work, it is only ferrous material that is of concern, so techniques such as spectroscopy, acid number, and moisture content are of little use for the purposes of the experimentation. The rig has been set up to with equipment to measure several parameters. These are: Two magnetic plug chip collectors positioned in the direct flow of the oil. Samples from these are taken each hour the rig operates. A 6 channel Vickers Industrial QDM® Oil Debris Monitoring System Series 3P3194 which counts particles by induction, retaining them for further analysis. The system records debris particles over 500 microns by induction. An ART Instruments Portable Oil Diagnostic System (PODS). The unit is compliant to ISO 11171 and has been set up for the purposes of the experiment to measure to MIL-STD-1246. Samples are taken every two hours. Samples taken from the magnetic plugs every hour along with a direct sample from the oil sump. The debris collected by the QDM system is also taken and these are further analysed using a Meiji microscope with DT frame grabber software. Two sets of time data from accelerometers placed on the shaft housing close to the test gears, along with a trigger signal are recorded using a DIFA measuring systems 16 channel, 16 bit data acquisition and analysis unit. 6 samples of 32768 points with a frequency band of 12.8kHz are taken for each hour of rig operation.
OIL SAMPLING To use particle analysis as an effective condition monitoring tool, it is essential that the samples taken from the system contain a representative selection of debris particles (Predict DLI). The debris
954
collection on the back-to-back gear testing rig was set up with this in mind and so the magnetic chip collectors were positioned in the direct oil flow in the tank. Debris should be collected from these by firstly dipping the plugs into a solvent to remove the oil from them and then by using adhesive tape to transfer the debris to glass slides for analysis under the microscope.
Figure 1: The Back-to-Back Gear Testing Rig.
CHI in Magnetic
Oil out
Chq> I j Collectors
P«rip«xTaiik Torque Plate Shaft Bearinp /
W^
DC Motor TestOcars
Drive Gears
A615 P h ^ Housing ^^« Figure 2: Schematic of the Rig's Arrangement. The Vickers QDM system has similar requirements in that it works best when in the full flow of the system. This was achieved by adding a pumping circuit to the rig with a fiher on the return feed so that
955
oil is constantly flowing through the A615 magnetic chip detector housing during the rig's operation (Figure 2). The PODS can be operated online or sample bottles can be used. For the purposes of this test, the sample bottle method was used and figures 3-6 show the trends over 24 hours of testing for the different particle sizes counted. The decrease in the number of smaller particles and increase in the number of particles over 25 microns is clearly visible over this period.
Particle Count -1 Micron 0) •§ ^ £ "5 ^ •O I 2
9.20E*07 8.70e-K)7 8.20e*07 7.706*07 7.206+07 6.70e*07 6.20E+07 5.70E+07 5.206*07
I:
Particle Count - 2 Microns
p^^''•M^^^g
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Fig 3: Particle Counts for 1 micron and 2 microns. Particle Count - 5 Microns
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Fig 4: Particle Coimts for 5 microns and 10 microns. Particle Count - 25 Microns
Particle Count -15 Microns
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z3 Reading
1.606+05 1.406+05 1.206+05 1.006+05 8.006+04 6.006+04 4.006+04 2.006+O4 0.006+00
Reading
Fig 5: Particle Counts for 15 microns and 25 microns.
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Particle Count - 1 0 0 Microns
Particle Count - 50 Microns
9m
2JOO
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Fig 6: Particle Counts for 50 microns and 100 microns.
Fig 7: ART Instruments Portable Oil Diagnostics System (PODS). After this initial period of testing there is little major damage to the gears, although oxidation has appeared on the teeth and minor pitting and scuffing damage is clearly visible to the naked eye. Two views of the test wheel are shown below after 20 hours of operation. Whilst the damage in the LH picture is by no means extensive, it appears to be from spalling or surface work hardening. There are, however, visible pits on the tooth (RH picture) suggesting that spalling is more likely.
Figure 8: The Wheel at 20 Hours.
957
CONCLUSION AND FURTHER WORK To date, a review of oil debris analysis techniques has been conducted, and a rig for the purpose of vibration testing and oil debris analysis has been put into operation. Preliminary results have been collected and further analysis and testing are necessary to continue the work in the direction specified. The work detailed in this paper shows some initial oil debris results fi-om the tests conducted on the back-to-back gear testing rig. Future work will gather data fi"om the wear-out of several sets of gears and will be used to detail the wear processes so that wear mechanisms can be detailed. Some goals for future work are listed below: Short Term: ^ • To plot the vibration data using vibration algorithms such as NA4 and FM4 (Dempsey 2000). • To classify the wear debris and chart this in a database according to shape, size and other features. • To use the data collected to investigate the fusion of multiple data sources. • To improve the image capturing techniques for capturing debris images. Long Term: • To develop a data fusion systemfi*ombased on results from vibration data and oil debris data generated in the tests. • To develop a prototype system which fuses data from multiple sources for the diagnostics of gearbox faults.
REFERENCES 1. Carl S. Byington, Terri A. Merdes and James D. Kozlowski (1999). Fusion Techniques for Vibration and Oil Debris/Quality in Gearbox Failure Testing. Condition Monitoring '99 Proceedings of the Litemational Conference on Condition Monitoring held at University of Wales, Swansea, UIC, pgl 13-128. Coxmoor Publishing Company 2. A Starr, J Esteban, R Willets and P Hannah (2000). Data Fusion for Advanced Condition Based Maintenance. Condition Monitoring 2001 - Proceedings of the International Conference Monitoring held at St. Catherine's College, Oxford, UK. Coxmoor PubHshing Company. 3. Paula J. Dempsey (2000). A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage. Comadem 2000 (p767). 4. Dana Martin and Joe Van Dyke, P.E (1998). Integrating Vibration, Motor Current, and Wear Particle Analysis with Machine Operating State for On-line Machinery Prognostics/ Diagnostics Systems (MPROS). PREDICT/DLL 5. Bradley Payne, Andrew Ball, Fengshou Gu and Weidong Li (2000) A Head-to-Head Assessment of the Relative Fault Detection and Diagnosis Capabilities of Conventional Vibration and Airborne Acoustic Monitoring. Proceedings of the 13* International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2000), Texas USA, pg233-242. December 2000. 6. M J. Neale and M Gee (2000). Guide to Wear Problems and Testing for Industry. Tribology in Practice Series. Professional Engineering Publishing. ISBN 1 86058 287 7. 7. J A. Williams (1994). Engineering Tribology. Oxford Science Publications. ISBN 0 19 856503 8. 8. Sxirapol Raadnui. The Analysis of Debris In Used Grease Samples. Condition monitoring 2000. pg 971-979.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) Crown copyright © 2001. Published by Elsevier Science Ltd. All rights reserved.
IDENTIFICATION OF NON-METALLIC PARTICULATE IN LUBMCANT FILTER DEBRIS G. C. Fisher and J. A. Hiltz Defence Research Establishment Atlantic Dartmouth, Nova Scotia, Canada B2Y 1Z7
ABSTRACT The examination of particulate debris dispersed in lubricant or trapped on a lubricant filter has long been used as a means of evaluating and monitoring equipment health. Traditionally this examination has focused on identifying and quantifying metallic wear particles as the means by which diagnostic information is retrieved. While the diagnostic value of metallic wear debris is of paramount importance to equipment condition monitoring, there are occasions when identification of non-metallic species is important. Such species can indicate lubricant degradation, thermal stressing of system components and the ingress of harmful contaminants. All too often the diagnostic value of non-metallic particulate is ignored or, at best, used to indicate lubricant change intervals. Three case studies are employed to demonstrate the use of two techniques, powder x-ray diffraction and polarized light microscopy, to identify non-metallic constituents of lubricant debris. KEYWORDS Condition monitoring, case studies, particulate, non-metallic, identification, x-ray diffraction, XRD, polarized light microscopy, PLM INTRODUCTION The aim of maintenance management is to ensure reliable, efficient operation of mechanical equipment. This is, in part, accomplished through the use of condition monitoring techniques to detect and manage component wear processes and to monitor lubricant degradation and
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cleanliness. The capabilities of the typical equipment health monitoring laboratory are therefore focused on those condition monitoring techniques that accomplish these tasks. Such techniques can include physical property testing and Fourier-Transform infrared spectroscopy of lubricants, vibration analysis and the use of data from on-line sensors. However, some of the most traditional and useful techniques are those that monitor wear metal content in the lubricant or filter debris. These include ferrography, atomic emission, inductively coupled plasma and x-ray fluorescence spectroscopy. Most of these techniques are designed to detect metallic particles. While this is understandable in light of the purpose of maintenance management, it does leave most equipment health monitoring laboratories somewhat lacking in terms of their ability to analyze non-metallic particulate. Situations occasionally arise where identification of non-metallic particulate in lubricant or filter debris is crucial to resolution of an observed problem. This report presents three such case studies. EQUIPMENT All x-ray diffractions were collected using a Philips 3040/00 diffractometer that utilized a Cu Ka (40 KV, 55 mA) incident beam with a 10mm mask and 1** divergence slit Diffracted xrays were detected using a PW1964/96 scintillation detector with a 1** anti-scatter slit and a 0.1 mm programmable receiving slit. Scans were run from 20's of lO*' to 100**. Reference diffraction patterns are from the International Center for Diffraction Data (ICDD), Swarthmore, Pa. Transmission polarized light microscopy was done using a Nikon Labophot-2POL microscope using a lOX 0.25NA strain free objective with a central dispersion staining stop. For Becke line testing, samples were mounted for in high dispersion Cargille""^ fluids of appropriate refractive index. RESULTS AND DISCUSSION Case Study Ul During the return voyage to home port after an extended refit, a Canadian auxiliary military vessel experienced problems with its steering gear. Frequent replacement of the lubricant filters was necessary due to excessive differential jwessures. As well, the hydraulic fluid (3GP-36 Grade 32) in the system was observed to be dark brown as opposed to its typical amber coloration. Laboratory examination of the lube oil filters indicated the presence of significant quantities of a fine white particulate. A similar particulate could be removed by filtration of the hydraulic fluid. It was inmiediately expected that the observed particulate was either a precipitated lubricant additive or an accumulation of wax deposits. Determination of the correct cause (source) would require identification of the particulate.
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One technique that can be used to identify powdered or particulate materials is x-ray diffraction. This technique takes advantage of the fact that crystalline solids consist of regular ordered atomic lattices that can, under the right conditions, interact with incident radiation leading to diffraction, Cullity (1978). Diffraction occurs when the Bragg equation: X - 2dsinO
(1)
is satisfied. In this equation X is the wavelength (in nm) of the incident radiation, d, or dspacing, is the distance (in nm) between the atomic planes in the lattice causing the diffraction, and 0 is the angle formed by the incident radiation and the atomic plane. In the typical powder x-ray diffraction experiment a specific x-ray wavelength (usually Cu Ka) is focused onto a sample and 9 is varied by rotating the sample. When the Bragg equation is satisfied diffracted x-rays are detected by an x-ray detector that moves in concert with the sample. By plotting detector response versus angle a diffraction pattern is obtained.
Figure 1: X-ray diffraction pattern of steering gear particulate. Figure 1 shows the diffraction pattern obtained from the white particulate removed from the steering gear filters. Similar results were obtained for the particulate filtered from the system's hydraulic fluid. Each peak in Figure 1 corresponds to an atomic plane whose orientation relative to the incident x-ray beam has satisfied the Bragg equation. Knowledge of A. and 0 permits calculation of the d-spacing for each plane. Some of the d-spacings calculated for the particulate are shown in Table 1. The calculated d-spacings in a diffraction pattern can be compared to literature values for known species to identify phases present in the sample. As demonstrated in Table 1, this pattern represents calcium palmitate.
961
TABLE I COMPARISON OF SAMPLE D-SPACINGS AND REFERENCE VALUES FOR CALCIUM PALMITATE (ICDD PATTERN 5-12) Reference 15.4 9.17 4.41 4.06 3.943 3.434 2.36
Sample 15.2 9.22 4.42 4.09 3.95 3.40 2.37
Palmitate salts can be used as thickening and corrosion protection agents for mineral oils. However, these materials are not used in 3-GP-36 hydraulic fluids. Discussions with system maintainers indicated that prior to refit a sufficient quantity of commercial corrosion inhibitor oil had been added to comprise 10% of the capacity of the steering gear lubricant system. This had been done to protect the components from corrosion during the refit, which did not include work on the steering gear itself It had been intended to monthly circulate the hydraulic fluid/inhibitor oil throughout the system. However, this had not occurred as planned. Further, the duration of the refit extended 18 months beyond the original completion date. It seems likely that the palmitate additive precipitated during the extended overhaul period and that this was not noticed until it was churned during the return voyage. Having only instrumentation available in a typical condition monitoring lab would have made identification of the particulate difficult. Precipitation of an additive would have been suspected as a possible cause of the incident and the presence of an inhibitor oil in the hydraulic fluid could have been indicated by viscosity and other physical property tests. However, identification of the particulate as calcium palmitate would have only been possible with Fourier transform infrared spectroscopy, and then only if the instrument had the capability of analyzing a solid material, as opposed to the more typical systems in condition monitoring labs which are designed for liquid analysis. Case Study i^2 A significant quantity of a hard black deposit was found in the centrifuge of an auxiliary diesel engine (MWM Deutz) on a patrol ftigate. X-ray diffraction of the deposit did not yield a particularly useful pattern, likely due to the presence of lubricant traces and amorphous carbon. A portion of the deposit was therefore heated for several hours at 800**C to remove these materials. Table 2 shows the d-spacings of the major peaks from this pattern and indicates that they correspond to a mixture of anhydrite (CaS04) and lime (CaO). To confirm whether these materials were actually present in the original deposit or were produced during the heat treatment, a portion was examined using transmittance polarized
962
light microscopy (PLM). Transmittance PLM is the study of optically transparent or translucent materials immersed in a fluid of known properties. TABLE 2 COMPARISON OF SAMPLE D-SPACINGS AND REFERENCE VALUES FOR ANHYDRITE (ICDD PATTERN 37-1496) & LIME (ICDD PATTERN 37-1497) Sample 3.49 2.85 2.78 2.47 2.40 2.33 2.21 1.701
Anhydrite 3.499 2.849
Lime
2.777 2.473 2.406 2.328 2.209 1.701
An important factor in this analysis is the refractive index, n. The refractive index of any material is the ratio of the speed of light in a vacuum (Cv) to the speed of the light in the material (Cm). Hence m
(2)
PLM has long been used to characterize particulates as comparison of morphology and refractive indices to literature values can identify the particle. The Becke line test can be used to measure refractive index. This test takes advantage of refraction phenomena to compare the refractive indices of two materials. Typically, a crystalline powder is placed in a liquid in which it is insoluble and this solid/liquid mixture is examined by transmittance PLM. Once the crystal is in focus, the focal plane is moved higher creating a halo either around or inside the particle. The halo is caused by refraction of light by the crystal. Movement of the focal plane up through the crystal causes the halo to move toward the medium of higher refractive index. Therefore placement of an unknown crystal into a liquid of known refractive index (liquids by definition have only one refractive index) will identify the crystal refractive indices as being higher or lower than that of the liquid. Through the use of several liquids of differing refractive index, an estimate of the crystal's refractive indices can be obtained. Mason (1983). The unheated powder from the diesel was placed in a fluid having a refractive index of 1.550. This examination indicated that the deposit primarily consisted of black non-crystalline particles (amorphous carbon). However, two different species of translucent materials were detected. One species had a single refractive index that was significantly greater than 1.550. Subsequent immersion in higher refractive index fluids indicated that the refractive index of this material was in fact greater than 1.800. Lime has a single refractive index of approximately 1.84. The other species had three refractive indices that were all slightly
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greater than 1.550. Anhydrite is monoclinic and has three refractive indices: 1.57,1.575 and 1.61. These results support the identification of anhydrite and lime in the deposit by x-ray diffraction. The lime is likely a precipitated oil additive as marine diesel lubricating oils often contain basic additives, such as CaO, to neutralize acidic species resulting from oil degradation or contamination. The presence of the anhydrite was somewhat more difficult to explain. There are two potential sulfur sources that can result in the formation of anhydrite. The first is from the oxidation of the sulfur in the distillate fuel. The second is the zinc dialkyl dithiophosphate-based additive present in the oil. Metal dialkyl dithiophosphates, including zinc diilkyl dithiophosphates, are commonly used as thermo-oxidative antioxidants. These chemicals act to decompose peroxides and inhibit the propagation step of the oxidation process shown in Figure 2, Al-Malaika (1983). 1. 2.
RH ^^^^"^ >R*+HOO* (a)R^W2->R00* {b)ROO*-\-RH -^ROOH-^R* 3. (a) ROO * +HOO* -> ROOH + Oi (b)ROO*^R*->ROOR {c)R*'¥R*-^RR 1. Initiation -formation ofhydroperoxide andalkyl radicals in presence of oxygen and metal ion 2. Propagation -formation ofa) alkylperoxide radical and b) regeneration ofalkyl radical 3. Termination Figure 2. Reactions involved in the oxidative degradation of hydrocarbon molecules in lubricating oils. The reaction between zinc dialkyl dithiophosphate and hydroperoxides, formed by the thermo-oxidative degradation of the hydrocarbons in diesel lubricating oils, are shown in Figure 3. One of the products arising from the reaction between the antioxidant and the peroxides is sulfur trioxide. In the presence of moisture, sulfur trioxide forms sulfuric acid and lime forms calcium hydroxide. Calcium sulfate is a product of the reaction of these two chemicals. The techniques employed by most condition monitoring labs are directed at identifying metallic species and characterizing lubricant degradation. As such, characterization of a deposit consisting of inorganic species would not be possible.
964
A^ S
HO.
Oft
-S
Figure 3: Reaction between zinc dialkyl dithiophosphate and hydroperoxides.
Case Study f^3 For a number of months in 2000, coolant systems for the CIWS guns of several CF ships were experiencing problems shortly after being returned from overhaul. Upon return and installation, each system would be filled with deionized water/glycol mixture and, upon running, the systems filter would quickly clog. Subsequent cleaning of the filters indicated that a black-green particulate was the culprit. An x-ray diffraction of the deposit was collected and Table 3 indicates that the minerals enstatite (MgSiOa) and forsterite (Mg2Si04) account for the observed d-spacings. To further verify the presence of these minerals the deposits were examined using transmittance PLM. The Becke line test indicated the presence of two different rhombohedral species having refractive indices of approximately 1.65. Enstatite and forsterite are rhombohedral magnesium silicates having refractive indices ranging between 1.64 to 1.68. Enstatite and forsterite mixtures are available commercially as silica-free blasting media. These materials were being used in the contractor's facility and were apparently entering the cooling system during maintenance work. A subsequently introduced flushing procedure prior to installation has alleviated the problem.
965
The identification of the particulate deposit in the gun coohng systems and subsequent determination of their origin would not have been possible using traditional condition monitoring methodologies. TABLE 3 COMPARISON OF SAMPLE D-SPACINGS AND REFERENCE VALUES FOR FORSTERITE (ICDD PATTERN 34-189) & ENSTATITE (ICDD PATTERN 35-610) Sample 3.90 3.18 2.88 2.52 2.46 2.35 1.752
Forsterite 3.881
2.510 2.457 2.346 1.748
Enstatite 3.170 2.874 2.523 2.455 1.759
CONCLUSION The case studies discussed here presented the capabilities of x-ray diffraction and transmittance polarized light microscopy in identifying non-metallic constituents of lubricant filter debris. These methods were able to characterize a precipitated organic oil additive, an inorganic thermal degradation by-product of diesel lubricating oil and a mineral contaminant. The analytical methodologies typically available in equipment health monitoring labs focus on identification of metallic species (to assess component wear) and the physical condition of the lubricant. While such techniques are capable of addressing the majority of condition monitoring problems, situations will arise where they cannot identify the occurring problem. It is not our intention to suggest that condition monitoring labs (rush out and, delete) purchase x-ray diffractomcters and PLM microscopes. (In fact,delete) Acquisition of such instruments and maintenance of operator expertise (for these techniques, delete) is costly, particularly for x-ray diffraction. However, it would serve such laboratories well to have access to these techniques through universities or commercial support agencies for the times they are needed.
REFERENCES Al-Malaika, S. (1983) Comprehensive Polymer Science- The Synthesis, Characterizations, Reactions and Applications of Polymers, Chapter 19, Volume 6 - Polymer Reactions, Pergamon, Toronto. Cullity, B.D. (1978). Elements of X-ray Diffraction, Addison-Wesley, Don Mills, Ontario. Mason, C.W. (1983). Handbook of Chemical Microscopy, Wiley & Sons, New York.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
INFLUENCE OF TURBINE LOAD ON VIBRATION PATTERN AND SYMPTOM LIMIT VALUE DETERMINATION PROCEDURES T.Galka Institute of Power Engineering 5 Augustowka St, Warsaw 02-981, Poland e-mail:
[email protected] ABSTRACT Technical condition diagnosis is based on relation between vectors of condition parameters and measurable symptoms. There are also other parameters that influence the latter. In machines that transform large amounts of energy, e.g. steam turbines, substantial influence of load should be expected. Little attention has been paid to this problem, apart from the statement that load should not be neglected in diagnostic symptom assessment. Experimental data show that vibration-related diagnostic symptoms exhibit marked influence of turbine active load and that there are considerable differences between various symptoms. Some general principles can, however, be found. Quantitative determination of relations between load and individual symptoms is a complex task, but experimental relations for individual machines (or groups of machines) can be estimated. These relations will obviously depend on machine layout and design features. Measured symptom levels may thus be 'normalized' with respect to turbine load, which allows for a more meaningful diagnosis. Load influence also turns out to be important for symptom limit value determination from experimental data. Modification of relevant procedures in accordance with previously developed theoretical model results in improved estimation accuracy. This is illustrated by several examples for utility steam turbines.
KEYWORDS technical condition, steam turbine, technical diagnostics, diagnostic symptom, limit value
INTRODUCTION Quantitative technical diagnosis is expected, in general, to determine the extent of damage of an element or object under consideration and to estimate its residual life. This is achieved on the basis of available diagnostic symptoms. In order to estimate how far has the lifetime consumption gone, and therefore to make well-founded decisions concerning future operation and necessary repairs, we obviously need some sort of scale. In many diagnostic systems a three-stage scale is used, involving (for a given symptom) three characteristic values: basic, limit and admissible one.
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Basic values can in principle be determined from reference measurements (see e.g. Orlowski and Galka, 1993), and admissible values are usually provided by machine manufacturer, on the basis of safety considerations. Limit values can be determined from the energy processor model (for details, see Natke and Cempel, 1997), which describes how technical condition evolution is represented in time histories of measurable diagnostic symptoms. This model employs certain simplifying assumptions and therefore allows for a neat mathematical description, comparatively easy for subsequent treatment. When dealing with raw experimental data, however, we seldom obtain a smooth, monotonic, increasing curve predicted by the model. Instead we have an irregular broken line, sometimes even with sections of negative slope. This is due to the fact that, apart from technical condition evolution, there are many other factors influencing measured symptom values. Which factors should be taken into account, and to what extent, will depend on a particular machine under consideration. For objects that transfdrm large amounts of energy (like, for example, steam turbines), we may intuitively predict that intensity of this process, given by output power or load, will influence residual processes power and therefore symptom values (Galka, 1999). This sounds quite obvious, but this issue has not drawn much attention, apart from quantitative and rather trite statement that this influence is not negligible and should be taken into account. Of course, accounting for machine load is important not only for symptom limit value determination. In practical diagnostics we often observe sudden and considerable increase or decrease of symptom value, too abrupt to have been caused by normal deterioration of technical condition. This is usually explained by some 'disturbance' or by random nature of phenomena under consideration. It should be worthwhile, however, to analyze if any of such 'disturbances' might be attributed to load variations, and to what extent. This certainly would allow for a more precise technical condition diagnosis.
BACKGROUND Theoretical background of symptom limit value determination procedures, based on energy processor model and symptom reliability concept, together with relevant mathematical treatment, have been described elsewhere (Cempel, 1993; Natke and Cempel, 1997). Here we recall only some results. With certain simplifying assumptions, power V of residual processes that accompany the principal process of energy transformation can be expressed as
where 0 denotes time, Ob is time to breakdown (which, within the framework of this model, implies ultimate 'death' of the object) and VQ = V(0 = 0). Power of residual processes is, in general, not measurable, so our inference on object technical condition is based on observable symptoms S: 5(0) =(^¥{6)],
(2)
where 0 denotes some symptom operator. With such simple equations, further treatment (for given types of symptom operator - see Orlowski and Galka, 1997) is quite straightforward and leads to convenient analytical relations. Such description, however, is not fully adequate for complex objects, especially if they are repaired during their operational life. As it has already been mentioned, there are other factors that influence S{0) and some of them must be taken into account. Adaptation of the basic model for steam turbines (Galka, 1999) should, in particular, include at least the following factors: 968
- logistic vector L (describing differences between individual machines and consecutive life cycles), - irreversible degradation factor h (results of which cannot be removed during overhaul or repair), - active load Pu. Even with reasonable simplifying assumptions, such development of the basic model brings about inevitable complication of mathematical description. In particular, time dependence of V (see Eq.l) becomes complex (Orlowski and Galka, 1999):
vxe.@) = v,,xx\-x^-^)'
(3)
where subscript / denotes i-Xh life cycle, 6^ is time that starts from zero at the beginning of object life {0 starts from zero at the begirming of each life cycle, i.e. after each overhaul or repair) and Xi=AL.KPu)
.
(4)
Logistic vector L is, for a given life cycle, assumed to be constant. We may also note that influence of irreversible degradation should be small if our data cover a period not very long by comparison with turbine service life (which is, say, thirty to forty years) and we haven't yet come close to its end. We may thus expect that, in certain circumstances, influence of load should prevail. This suggest practicability of experimental evaluation of this influence. It must be kept in mind that relation between load and residual processes power (and hence symptom vector) is not of the 'direct' type; it might be more appropriate to say that they both depend on the same vector of parameters that include steam pressure and temperature, control system settings, condenser vacuum, ambient temperature and many more.
EXPERIMENTAL DATABASE The above considerations refer to all types of diagnostic symptoms. In our work we have relied mainly on vibration-based ones, which are known to provide much information on machine condition while being relatively simple to acquire and process. All data discussed below refer to 200 MW, 3000 rpm steam turbines, of which over sixty are now operated by Polish power industry. These machines were produced in Russia and Poland and installed in Polish power stations from early 60s to early 80s; some of them were later modernized (mainly lowpressure turbine and control system). We chose a group of eleven turbines in three power stations which, apart from minor details, may be considered identical. Their operational life was about 160,000 to 180,000 hours, so we may expect that influence of irreversible degradation has not become predominant and its advance during the period covered by our study (about two years) has been small. Each turbine-generator unh has nine vibration measurement points (seven bearings plus two points on the low-pressure casing). We measured absolute vibration velocity in the range up to 10 kHz and used 23% CPB power density spectra. Frequency bands are characterized by their mid-values. For each point we analyzed vibration levels in bands determined from the turbine vibrodiagnostic model (see e.g. Orlowski and Galka, 1993), each level being considered an individual symptom. Given three measurement directions (vertical, horizontal and axial), we obtain a set of 240 symptoms for each machine. All measurements were taken during steady-state operation, in most cases with automatic frequency and load control. This means that during a single measurement cycle (about twenty minutes) load may change within narrow limits, by a few megawatts. Because of this, full load range, i.e. from 140 to 205
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MW, was divided into 5 MW intervals and results were averaged within each interval. In order to provide comparison between different symptoms, results of this averaging were related to the mean value from all measurements.
RESULTS OF ANALYSIS Due to differences in vibration generation mechanisms, frequency range covered by this analysis can be divided in two sub-ranges, conventionally referred to as 'harmonic' and 'blade' ones. The former contains vibration components resulting directly from rotary motion; in our case, first four harmonic and the 0.5yo sub-harmonic components are taken into account. The latter includes vibration components generated as a result of interaction between steam flow and turbine fluid-flow system, their individual frequencies being thus determined by number of blades in individual stages. In the case of 20b MW turbines considered there this range spans from about 1 kHz to about 8.2 kHz. Altogether we have 135 symptoms from the harmonic range and 105 symptoms from the blade one. It might be expected that relations between turbine load and vibration velocity level in these two frequency sub-ranges should exhibit considerable differences and data analysis confirms this prediction. From the quantitative point of view, load dependence of vibration symptoms is stronger in the blade range: if mean value of all readings of a given symptom is taken as 100%, we have average minimum of 40.4% and average maximum of 170,4% for 105 symptoms from this range. Corresponding values for 135 'harmonic' symptoms are 57.7% and 155.3%, respectively - the difference is thus evident. It is interesting to note that this is consistent with everyday diagnostic experience, which shows that vibration levels in the blade range are more sensitive to even small changes of turbine operational parameters. Most experimental symptom vs. load curves in the harmonic range exhibit certain regularity. For low loads, approximately below 160 MW, these curves are quite steep, then become comparatively flat and, at the very end of the entire load range, slope increases again. Typical examples are shown in Fig.l. For highest load levels slope is usually positive, while for the lowest ones it can be either positive (Fig. I a) or negative (Fig. lb). Extrema of the load-symptom relations are thus typically close to either lower or upper limit of the load range, which is clearly seen from data shown in Table 1. This leads to a conclusion that, in order to minimize load influence in procedures involving experimental data processing (e.g. symptom limit or basic value estimation), we may just, as a simple measure, reject data obtained for the lowest and highest load levels. In the blade frequency range, corresponding curves are far less regular and in most cases their slopes are markedly higher. Some conclusions, however, are similar. Distribution of extrema among individual load intervals is more 'uniform' than for harmonic components, but still most of them are found in extreme ones (see Table 1). We may thus conclude that also in this case rejection of data obtained at very low or very high load should improve accuracy of relevant procedures. In the case of 200 MW turbines considered here, this 'useful' load range may be estimated as 165 -^ 200 MW. It should be noted that for turbines operated in the base-load mode, which seldom operate at extreme loads, this is not a particularly great loss of data - in this very case, only about 13% of all measurement results is rejected. Although curves in the blade frequency range are less regular, they have some common characteristic features that may be related both to design and to operational characteristics. Symptoms related to vibration components generated by the low-pressure turbine fluid-flow system in most cases exhibit substantial decrease for the highest load levels, while the remaining part is often more or less monotonic; two examples are shown in Fig.2. This is explained by the fact that highest load can be attained and maintained only with high steam flow rate and at low condenser pressure (i.e. high 970
vacuum). In such conditions, steam flow is regular and uniform, with no backwards flow (which occurs at lower load, especially with poor condenser vacuum), and vibration generation is less intensive. 200 MW turbines bearing 3 axial, 100 IHz band
170
180
Load, IVIW
(a) HP-IP bearing, vertical, 100 Hz 200 M\N turbines bearing 2 vertical, 100 Hz band
170
180
Load, MW
(b) rear IP bearing, axial, 100 Hz Fig. 1. Examples of experimental symptom vs. load curves in the harmonic frequency range. Crosses denote values derived from experimental data and continuous curves are least-square fits. Right-hand slope is in most cases positive, while left-hand one can be either negative (a) or positive (b). For symptoms related to the high-pressure turbine fluid-flow system, mainly to the rotor stages, there is often a considerable increase at low load levels. This is most probably caused by steam flow asymmetry, resulting from the group-type control system, employed in these turbines. They are fitted with four distinct groups of high-pressure inlet vanes, each fed via its individual control valve. Last of these should, in principle, open only above the rated load. This means that at low load steam flow through the high-pressure turbine is asymmetrical and non-uniform, and hence vibration components generated by its fluid-flow system are higher.
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200 MW turbine LP casing front horizontal, 5000 Hz band
170
180
Load. MW
(a) LP casing front, horizontal, 5000 Hz 200 MW turbine Bearing 4 axial, 4000 Hz
170
180
Load, MW
(b) Front LP bearing, horizontal, 4000 Hz Fig.2. Examples of experimental symptom vs. load curves in the blade frequency range for the LP turbine. Crosses denote values derived from experimental data and continuous curves are least-square fits. Most symptoms related to the LP turbine exhibit marked decrease for highest load levels. In practice there are often problems with maintaining rated steam pressure and temperature, caused mainly by boiler-related troubles, so necessary load can be maintained only at higher flow rate. Fourth control valve thus opens at rated load or even below. Steam flow rate becomes higher, but at the same time the flow is more uniform. This explains why, in most cases, symptom vs. load curves for the high-pressure turbine - especially at the side of steam inlet, i.e. HP-IP bearing - show considerable negative right-hand slope. On the other hand, higher steam flow can cause slight increase of 'harmonic' symptoms and, as a result, curves for two types of symptoms for the same measurement point almost resemble mirror images. Example is shown in Fig.3.
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Table 1 Extrema of experimental symptom vs. load curves in individual load intervals Load range from - to 141-145 146-150 151-155 156-160 161-165 166-170 171-175 176-180 181-185 186-190 191-195 196-200 201-205
Number of extrema blade frequency range harmonic frequency range max. min. min. max 2 21 12 10 12 13 7 23 7 2 12 7 8 18 7 28 18 16 17 25 6 1 2 0 5 5 4 8 1 0 0 12 7 0 0 3 5 0 3 13 13 2 7 3 0 0 8 9 34 25 26 13
APPLICATION Experimental vibration database is often used to estimate basic and limit vibration levels. This is particularly important when a diagnostic system, even a simple one, is introduced for machines already in operation for a considerable time. Input data for relevant algorithms should be consistent, which means that they should be obtained in comparable conditions. This refers also to machine operational parameters, including load. In principle, we can introduce load influence in formulae that describe relation between power of residual processes and extent of technical condition deterioration (see previous sections); we only have to provide mathematical description of curves such as shown in Figs.l, 2 and 3, which is no special problem. Then it is possible to derive relevant equations for S(9) relations and employ procedures similar to those used for the basic energy processor model (Cempel, 1993). This, however, leads to complex and unwieldy relations. It is more convenient to 'normalize' measured symptom values with respect to some reference conditions and then use such data for straightforward calculations. Such 'normalization' is a slightly time-consuming, but basically simple numerical procedure. Obviously, if special procedures and reference data are implemented in a diagnostic symptom, it can be performed concurrently with data acquisition. It is worth noticing that in many cases 'normalized' data yield better fitting of experimental vibration level histograms, which is an indirect proof of method's suitability. Example is shown in Fig.4. Judging from current experience, introduction of such 'normalization' with respect to load in symptom limit level estimation procedures results in a change of a calculated value by, in most cases, a few percent. Example is shown in Table 2. It is noteworthy - and in fact quite surprising - that, despite considerable influence of load on ^'ibration levels in the blade frequency range, estimated limit values are (with just one exception) almost the same for all three data selection procedures. In most cases limit values shown in Table 2, estimated from 'normalized' data, are slightly higher than those estimated from 'raw' data, but this refers to just this example and certainly is not a general rule.
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200 MW turbine Bearing 2 horizontal. 160 Hz 200 180 160 0
140
1 120 o
I 100 M 0)
>
80
0^
60 40 .0
20
170
180
Load, MW
(a) 50 Hz 200 MW turbine Bearing 2 horizontal. 3150 Hz band 160 140 120 100 80 60 40 20
140
150
160
170
180
190
200
210
Load, MW
(b) 3150 Hz Fig.3. Examples of experimental symptom vs. load curves for the HP-IP bearing, vertical direction. Crosses denote values derived from experimental data and continuous curves are least-square fits. Most symptoms relating to the HP turbine from the harmonic frequency range exhibit positive slope (a), while in the blade frequency range this is reversed (b). Accounting for load influence obviously improves limit value determination accuracy and, for this one reason, should be introduced, if only relevant relations are known. In most cases resulting change is not large, but sometimes can even reach about 10%. It seems that first two harmonic components are most sensitive, but again this is only a preliminary conjecture, based on available data. As work is still in progress, more detailed conclusions shall probably follow.
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Table 2 Comparison of vibration limit levels (200 MW turbine, rear IP bearing, vertical direction) Frequency range
harmonic
blade
Frequency band [Hz] 25 50 100 160 200 1600 2000 2500 3150 5000 6300
Limit level [mm/s] estimated from 'normalized' data 165 to 200 MW all data 0.225 0.226 0.225 3.332 3.362 3.209 3.889 3.633 3.635 0.926 0.909 0.905 0.918 0.914 0.924 0.127 0.129 0.129 0.096 0.097 0.096 0.160 0.160 0.157 0.189 0.196 0.195 0.385 0.386 0.380 0.120 0.107 0.108 200 MW turbine Bearing 4 vertical, 50 Hz
6,5 (6;6,5]
Vibration velocity, mm/s
200 MW turbine Beraing 4 vertical, 50 Hz
7 Vibration velocity, mm/s
Fig.4. Experimental vibration velocity histograms: top, 'raw' data; bottom, 'normalized' data (front LP bearing, vertical direction, 50 Hz band). Continuous lines show Weibull distribution fitting (for details, see Orlowski and Galka, 1997). 975
ACKNOWLEDGMENT Results used and quoted in this paper have been obtained with the support of the State Committee for Scientific Research, within the framework of the 7 T07B 04116 Research Project.
REFERENCES Cempel C. (1993). Theory of energy transforming systems and their application in diagnostics of operating systems. Applied Mathematics and Computer Science 3:3, 533-548. Galka T. (1999). Application of energy processor model for diagnostic symptom limit value determination in steam turbines. Mechanical Systems and Signal Processing 13:5, 757-764. Natke H.G. and Cempel C. (1997). Model-Aided Diagnosis of Mechanical Systems. Springer-Verlag, Berlin-Heidelberg-New York. Orlowski Z. and Galka T. (1993). Influence of turbine modernization on its basic vibration spectra. Proceedings of the COMADEM'93 International Congress, University of the West of England, Bristol, 442-448. Orlowski Z. and Galka T. (1997). Symptom reliability - a new tool for quantitative assessment of turbine technical condition. Proceedings of the lASTED Conference 'High Technology in the Power Industry', Acta Press, Anaheim, 65-70. Orlowski Z. and Galka T. (1999): Effect of material degradation processes on vibration limit level determination procedures. Proceedings of the COMADEM'99 International Congress, Coxmoor Publishing Co., Oxford, 371-379.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
STUDY ON THE MOVEMENT REGULATION OF GRINDING MEDIA OF VIBRATION MILL BY NOISE TESTING
Jiang Xiaohong, Pu Yapeng and Zhang Yongzhong College of Mechanical and Electronic Engineering, China University of Mining and Technology, Xuzhou City, Jiangsu Province, China
ABSTRACT The physic meaning of testing and analyzing the noise intensity of vibrating mill is discussed in this paper. Through a series testing of noise intensity on the sample machine, the influence of vibrating amplitude and frequency of vibrating mill to the variation trends of noise intensity are studied. The range of peak point of noise intensity emerged is investigated under the condition of large amplitude. Through analyzing equal noise intensity, the operating parameters to meet different pulverizing requests are determined, that is, favorable operation parameters for large granularity material or for ultra-fme grinding are given out respectively. KEYWORDS Vibrating mill, grinding media, equal noise intensity, testing
INTRODUCTION For today Nanometer technique developing vigorously, the role of ultra-fme pulverizer become more and more important. Vibrating mills are ideal equipments for ultra-fme grinding. How to adjust the kinetic parameters and dynamical parameters of vibrating mill to make the grinding media reaching the best movement state, are the key problems for further increasing productivity and improving products quality. It is the subject that the scholars in the field have been investigating. Due to the randomicity and non-linearity of grinding media movement, it makes the problem become quite
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complex. In this paper, in order to investigate the movement regulation of grinding media collective, a series testing have been made. The investigating experiments include two parts: vibrating signal testing and noise testing. The method and results analysis about vibrating signal testing have referred in another paper [1], this paper only discuss about noise testing. PHYSICS MEANING OF NOISE TESTING The mutual effects among media or between media and the cylinder will give out sound. This effect can be divided into two forces: vertical force and transverse force. Vertical force results in change of the velocity direction of grinding media, while transverse force make the media revolving. Obvious, the sound intensity produced by vertical force is greater than that produced by transverse force. Given energy of sound is Ea, and the dissipative heat energy by media friction is En, and the total wasting energy of mill E can be decided: E = E a + En
(1)
When materiel is not filled, grinding media surface and the wall of cylinder are smooth, friction coefficient is very small, then E a » En, so: E«Ea
(2)
That is, the total wasting energy of mill equal nearly to sound energy. According to the conclusion, it can be educed several points of view as follow: (1) If the measured sound intensity level of a mill is equal, it can be considered, then, at the different measuring time, the total wasting energy of the mill is equal. (2) If the absorb power of a mill have the same quantity at different time, then the sound intensity level is nearly the same. (3) If the operating parameters are in high energy level, that is, the amplitude, frequency and filling rata are all in their large value, but the sound intensity is low, then it can be considered that transverse force is prominent while the vertical force is small. Whereas, if the amplitude, frequency and filling rate are all in low value, but the sound intensity is in high level, then it can be considered that vertical force is prominent while the transverse force relatively small. It needs to say, to understand relatively prominence degree in movement of the grinding media collective has great practical meaning for powdering operation. It has been known, for higl^ efficiency break up the grain of large dimension, the vertical force among grinding media or media with the wall of cylinder expects great. While if the materiel is needed to mill as fine as possible, the transverse force in the movements of grinding collective expects great, so that granularity can reach a higher class. The output of powder is always a contradiction with the granularity. But if the operation parameters could be well adjusted, it can meet different pulverizing requests. NOISE TESTING AND RESULTS ANALYZING
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Design of Testing Device The adjustable operation parameters of vibration mill are mainly vibrating amplitude A, the rotate speed of the vibration mill n and the filling rate of the m i l l / The vibration mill for testing has several qualifications as follow: The amplitude of vibrating mill is adjustable (0'^20mm) ; The rotate speed of the mill motor can be changed continuously (300-1338rpm); filling rate is in the range of (30-85%). The diameter of vibration mill for testing is 200mm. Grinding media is round bar; its diameter is 12mm. Noise intensity and vibrating testing system as Figure 1. Vibrating Mill
' 1 1
3 562A Dynamic
Acceleration
Impulse
Transducer
Shock Meter
Recorder
Signal Analyzer
—J
Sound meter Oscillograph
Figure 1. Noise Intensity and Vibrating Testing System Experiments Methods of Noise Testing In order to investigate the movement regulation of grinding media collective, a series testing have been made. The purpose of these testing is mainly to determine the movement regulation of grinding media collective at different amplitude, rotate speed and filling rate. At the same time, try to elicit some regularity conclusions firom testing results analyzing and provide experiment groundwork for theory modal. The noise test consists of two parts: (1) The vibrating amplitudes of the mill: 3mm, 6mm, 9mm; the vibrating frequency: 300rpm, 500rpm, 700rpm, 900rpm; the filling rate: 30%, 50%, 70%, 85%. Combining the three parameters, there are 44 experiments. (2) Noise intensity testing and signal analysis under equal vibration intensity of mill. Vibration intensity are 3g,4g, 5g,6g, g=9.8m/sec^; The vibrating amplitudes of the mill: 3mm, 5mm, 6mm, 7mm, 9mm, 13mm, 15mm; The filling rate of mill: 70%). Combining the three parameters, there are 28 experiments. The Results Of Noise Intensity Testing And Analysis Noise Intensity Testing of Different Vibrating Amplitude (A), Frequency (n) and Fill Rate (f) Fig.2-Fig.4 express the variation trend of noise intensity at the same amplitude but different rotation speed, taking the filling rate as horizontal coordinate. It can be seen, as the rotate speed increasing, the noise intensity increases obviously, but the variation trend of noise intensity is not the same. As the filling rate increasing, the noise intensity appears the increasing trend. Only one exception, on the situation ^=3mm,/=70% that disobey this rule. The noise intensity of y4=3mm,^70%) is less than that of^=3mm, /=50%. 979
Fig.5~Fig.7 given out the variation trend of noise intensity at the same amplitude but different filling rate, taking the rotate speed as horizontal coordmate. It can be seen, as the filling rate increasing, the noise intensity appear basic the increasing trend, but not obvious; noise intensity increases monotonously; the curves of noise intensity have nearly the same shape. The noise intensity of
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Figure 10: When /^70%, the influence of n and A to the noise intensity
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Figure 11: When /=85%, the influence of n and A to the noise intensity
Fig.8~Fig.ll show the variation trend of noise intensity at the same filling rate but different vibrating amplitude, still taking the rotate speed as horizontal coordinate. It can be seen, as the amplitude increasing, the noise intensity increases; variation trend of noise intensity appears increasing monotonously, and the curves have almost the same shape. }^oise Intensity Testing Of Equal Intensity Vibration Taking the amplitude as horizontal coordinate. Fig. 12 given out the variation trend of noise intensity at the condition of 70% filling rate but different vibrating intensity. Though vibrating intensity is the same, the noise intensity is not the same. It can be seen fi-om the figure, the noise intensity have similar variation trend under the four vibrating intensity. The change of the amplitude has something to do with curve variation trend. In the range of ^=3~5mm, the amplitude has great influence to the noise value, while in the range of 5mm
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Figure 13: When/=70%, the influence of vibrating intensity and A to the noise intensity
l>loise Signal Analysis From the graph of signals of time domain and frequency domain, it can be seen that the frequency contained in noise signal are very complex. The frequency band is quite wide, the signal between 0 to 2kHz is very abundant, and there is no prominent frequency, as approximately broadband noise. This indicates that the movement of grinding media collective is clear nonexistence rule. CONCLUSION (1) Analyzing the general trend of noise intensity testing, we learn that, in the condition of great amplitude and low speed, the noise intensity is low. In small amplitude but high rotate speed situation, noise intensity is high. That is, the under the condition of great amplitude and low speed, the transverse force of grinding media collective is prominent while vertical force is weak. This is not advantageous for materiel smash, but is favor to fine milling. In small amplitude but high rotate speed situation, the vertical force of grinding media collective is prominent while transverse force is weak; it is beneficial to materiel smash. This is corresponding to the results of vibrating testing in paper [1]. (2) Under the condition of equal vibrating intensity, the valley area of graph in noise intensity trend
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indicates that even in high rotate speed, there still exist the situation that transverse force of grinding media is dominant. This area is very important. Working in this area, the vibrating mill can enhance fine milling effect, at the same time; it can reduce the disadvantageous influence to the equipments caused by great amplitude. (3) Under the condition of equal vibrating intensity, the peak point of noise intensity in great amplitude area ( ^ ^ 8 mm) is caused by the influences of rotate speed and amplitude to the noise intensity. When vibrating intensity is high, choose the operation parameters correspond to the peak point, it can increase the smash rate of materiel, at the same time; reduce the disadvantage influence to the equipments due to high rotate speed. References 1 Jiang Xiaohong, Pu Yapeng and Zhang Yongzhong. (2000). Study on the Movement Regulation of Grinding Media of Vibration Mill by Vibration Testing. Proceedings of the First International Conference on Mechanical Engineering, China Machine Press, Beijing, China, 405-406 2 Pu Yapeng. (1997) .The Research on Efficiency Improving of Ball Mill and Movement Regulation of Media Collective of Vibration Mill. Bachelor Thesis, Jiangsu, Xuzhou, China, 61-74
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
GAS TURBINE BLADE AND DISK CRACK DETECTION USING TORSIONAL VIBRATION MONITORING: A FEASIBILITY STUDY Ken Maynard', Martin Trethewey^, Ramandeep Gill ^, ' Applied Research Laboratory, The Pennsylvania State University ^ Dept. of Mechanical Engineering, The Pennsylvania State University State College, PA 16801, USA
ABSTRACT The primary goal of the this paper is to summarize field demonstrations of the feasibility of detecting changes in blade natural frequencies (such as those associated w^ith a blade or disk crack) on operating turbines using non-contact, non-intrusive measurement methods. This paper primarily addresses the results of application of this non- intrusive torsional vibration sensing to a large wind tunnel fan and a jet engine high-pressure disk. During the operation of rotating equipment, blade natural frequencies are excited by turbulence, friction, and other random forces. These frequencies couple with shaft torsional natural frequencies, which may then be measured. Laboratory testing was conducted to affirm the potential of this method for diagnostics and prognostics of blade and shafting systems. Field installation at the NASA Ames National Full-Scale Aerodynamic Facility (NFAC) reaffirmed the ability to detect both shaft and blade modes. Installation on a high-pressure (HP) disk in a jet engine test cell the manufacturer's facilities demonstrated that thefiindamentalmode of the turbine blades was clearly visible during operation. The results of these field tests have resulted in high confidence that this technique is practical for diagnosing and tracking blade and disk cracks. KEY WORDS Disk cracking; blade cracking; condition-based maintenance; failure prediction; torsional vibration.
BACKGROUND The detection of blade and shaft natural frequencies in the torsional domain requires that the signal resulting from excitation of the rotating elements by turbulence and other random processes is measurable. If measurable, these mtural frequencies may be tracked to determine any shifting due to cracking or other phenomena affecting torsional natural frequencies. Difficulties associated with harvesting the potentially very small signals associated with blade shaft vibration in the torsional domain could render detection infeasible. Thus, transduction and data acquisition must be optimized for dynamic range and signal to noise ratio (Vance, 1988; Maynard and Trethewey, 1999; Maynard et
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al. 2000). An overview of the laboratory results may be found in Groover, 2000, and Maynard and Trethewey, 2000. The advantage of using shaft torsional natural frequency tracking over shaft lateral natural frequency tracking for detecting cracks in direct-drive machine shafts is twofold •
A shift in natural frequency for a lateral mode may be caused by anythmg which changes the boundary conditions between the rotating and stationary elements: seal rubs, changes in bearing film stifihess due to small temperature changes, thermal growth, misalignment, etc. So, if a shaft experiences a shift in lateral natural fi'equency, it would be difficult to pinpoint the cause as a cracked shaft. However, none of these boundary conditions influence the torsional natural frequencies. So, one may say that a shift in natural fi'equency in a torsional mode of the shaft must involve changes in the rotating element itself, such a crack, or perhaps a coupling degradation.
•
Similarly, finite element modeling of the rotor is simplified when analyzing for torsional natural frequencies: these boundary conditions, which are so difficult to characterize in rotor translational modes, are near non-existent in the torsional domain for many rotor systems. This means that characterization of the torsional rotordyanamics is more straightforward, and therefore likely to better facilitate diagnostics.
Detection of the small torsional vibration signals associated with blade and shaft natural frequencies is complicated by transducer imperfections and by machine speed changes. The use of resampling methods has been shown to facilitate the detection of the shaft natural frequencies by: (1) correcting for torsional transduction difficulties (Maynard and Trethewey, 1999) resulting from harmonic tape imperfections (printing error and overlap error); and (2) correcting errors as the machine undergoes gradual speed fluctuation (Maynard et al. 2000; Groover, 2000). In addition, correction for more dramatic speed changes \^as addressed by Groover (2000). These corrections made laboratory testing quite feasible. Transducer setup and methodology The transducer used to detect the torsional vibration of the shaft included a shaft encoded with black and white stripes, an infrared fiber optic probe, an analog incremental demodulator and an A/D converter. Figure I shows a schematic of the transducer system. Fiber optic cable A-D converter
Fiber optic probe
1
m
Shaft encoded with equally spaced black and white stripes
Figure I: Schematic of transducer setup for torsional vibration measurement
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The implementation of the technique under laboratory conditions was previously presented in Maynard and Trethewey, 1999, and Maynard et al 2000. Figure 2a shows the tabletop rotor with eight "blades", and the resulting spectrum J^igure 2b) shows the blade group, and the individual detuned blade ("rogue blade").
Figure 2: Torsional Spectrum of Laboratory Rotor with One Detuned Blade
APPLICATION TO AGING TURBINES In vehicular turbines, the loss of turbine blades has resulted in accidents and fatalities. With the aging of commercial and military fleets, fatigue cracking and failure of discs and blades becomes a more pressing issue. For example, some engines in one specific family of engine s (currently used in about four thousand commercial aircraft) are approaching twenty years of age. Although these engines experienced little blade and disk cracking early in their lives, cracking has been detected during regularly scheduled inspections on aging engines. There is some concern that this cracking may accelerate as the engines continue to age, and may require increased inspections. The possibility of a crack precipitating and resulting in blade failure between scheduled inspections increases with age, and an in situ system could provide the user/maintainer with blade health information. Similarly, military aircraft engines are exhibiting symptoms of aging, which include blade and disk failure. From 1982 to 1998, 55% of USAF engine caused mishaps were related to high-cycle fatigue (HCF) of engine parts (Davenport, 1998), predominately blades and disks at the blade attachment (Davenport, 2001). In additbn, about 87% of the risk management inspections were related to this HCF. Thus, a large portion of the maintenance budget is associated with HCF of blades and disks at the blade attachment. NASA AMES NATIONAL FULI^SCALE AERODYNAMIC COMPLEX (NFAC) FANS The NFAC facility is the largest wind tunnel in the world, able to test a full-scale 737 in the largest section (see Figure 3). The fans used to drive the wind tunnel, shown in Figure 4, have experienced some blade cracking in the past, and the repairs made continued inspection unpractical. NASA decided to use modal impact testing of the blades to track the natural frequencies of the blades to detect shifts that might be associated with cracking. However, it was determined that the frequency might shift as much as ten percent simply by rotating the rotor 360° and retesting the same blade. Since this was on the order of the expected shift due to cracks, other methods were considered. A feasibility study was conducted using torsional vibration on an operating fan.
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•O'WTEsrsecTroN
Figure 3: National Full-Scale Aerodynamic Complex (NFAC) at NASA Ames
Figure 4: NFAC Fans The shaft was encoded with zebra tape, and testing was performed during operation of the fans. The results are shown in Figure 5. The first two peaks correspond to overall shaft torsional frequencies, based on simple dynamic models developed by the vendor in the 70s. The third peak, near 13.5 Hz, corresponds to the blade group. Of particular interest is the tight packing of the fifteen blade modes, especially knowing that the impact testing on a single blade could vary by 1 Hz or more during a single test session. We believe that the variable pitch mechanism (VPM) docs not provide a repeatable boundary support for the blades when it is not operating, but provides uniform and repeatable results during operation, when the VPM is preloaded. Subsequent to this testing, impact testing was performed on a blade with the oil pump operating to attempt to allow torsional coupling. The resulting naturalft^equencywas found to be about 14.6 Hz This testing demonstrated the ability of this measurement system to detect blade natural frequencies during operation of the NFAC fans.
Figure 5: Averaged Torsional Spectrum of NFAC Fan
INSTRUMENTATION OF AN AIRCRAFT JET TURBINE The HP turbine of a commercial jet engine was instrumented to detemiine the feasibihty of detecting the blade natural frequencies during operation using torsional vibration. The testing was perfomied in a warm air test facility, under load at about 9400 RPM. Figure 6 shows the fully assembled commercial engine and the HP disk as installed in the warm air test facility.
Figure 6: a) Fully Assembled Jet Engine; b) HP Disk in Warm Air Test Facility Several individual blades were tested by the engine manufacturer to determine the natural frequencies. These blades were fixtured to simulate the boundary conditions during operation. The modal testing results are summarized for the three specimen blades in Table 1. The instrumentation included a 200-stripe zebra tape and fiber optic probes. In addition, the output from a 60-tooth speed encoder was used as backup. The zebra tape did not survive the test, and the speed signal from the 60-tooth gear was used. The use of the 60-tooth wheel limited the frequency range of the data to onehalf the number of teeth times the
iBIade F12 Blade C14 Blade B7 1 Average 1 Std. Dev.
Mode 1 2450 2413 2434 2432 19
Mode 2 3943 3853 3875 3890 47
Mode 3 6038 6091 5994 6041 49
Mode 4 1 8409 8469 8364 8414 53
Table 1: Results of Modal Testing of Sample Blades
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speed of the shaft, or about 4500 Hz. Figure 8 shows the torsional spectrum of the HP turbine rotor. Note that torsional natural frequencies of the shafting system should change very little with speed, and coupled blade and shaft torsional modes increase very slightly with speed due to stiffening by axial force. So, we look for fequencies that are not shifting with speed. Note that the first peak at about 2400 Hz is close to the first mode from the blade modal testing. Earlier experimental and analytical work, however, indicates that blade natural frequencies can be significantly altered by Figure 7: 60-tooth speed encoder coupling with shaft torsional modes (Maynard and Trethewey, 1999; Maynard et al. 2000). It was shown that, for a small desktop rotor with much larger blade to rotor mass ratio, the difference might be more than 30%, and that the frequency may be higher or lower than rig impact testing results. Although intuition might imply that for relatively small blades coupling would be less of a factor, the effects of torsional coupling should be clarified using a dynamic torsional model. -lU
9000 rpm. 60 tooth -16 •
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2600
2800
3000 3200 3400 Frequency. Hz
- '"'iPiipi 3600
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4000
4200
Figure 8: Torsional Spectrum of HP Turbine
SUMMARY AND CONCLUSIONS The techniques developed for detecting torsional natural frequencies in the laboratory were implemented on a large wind tunnel fan that has experienced blade cracking, and a jet engine that has experience cracking in the disc at the blade root. The goals of the implementation project were to demonstrate the feasibility of field application in fans and turbines, and to establish a baseline for each class of machine. The data acquired clearly demonstrated the feasibility of field implementation, and established baseline natural frequencies for blades and shafts. However, interference from tape related spectral content was experienced. This interference was not experienced in the laboratory due to shaft size, access, and environmental differences. It is believed that this spectral content is associated not with tape printing error or overlap, but was introduced by the installation. This may be overcome by using a more precisely fabricated encoding device (such as the
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60-tooth speed encoder at the jet engine test facility), or by developing correction algorithms for the installation error. Future work Research to establish the size of detectable cracks in each class of machine is needed. For instance, turbine engines have experienced cracking in the HP disk at the blade root. It is important that we estimate the size of the detectable crack and the remaining useful life of the disk at the frequency shift detection threshold to establish the torsional vibration method as practical in field machines. Correction of the installation errors miist be accomplished to remove ambiguity and make the technology widely accessible. This work is currently underway. I
Acknowledgement This work described herein was supported by the Southern Company through the Cooperative Research Agreement Torsional Vibration and Shaft Twist Measurement in Rotating Machinery (SCS Contract Number C-98-001172); NASA Ames Research Center Grant Condition-Based Monitoring of Large-Scale Facilities, (Grant NAG 21182); by Multidisciplinary University Research Initiative for Integrated Predictive Diagnostics (Grant Number NOOO14-95-1-0461) sponsored by the Office of Naval Research; and by NASA Glenn/GE Aircraft Engines Turbine Disk Crack Detection Using Torsional Vibration: A Feasibility Study (GEAE Purchase Order 200-1X-14H45006). The content of the information does not necessarily reflect the position or policy of the United States Government, and no official endorsement should be inferred.
REFERENCES: Davenport, Otha B. (1988). Symposium,
Operational Readiness and High Cycle Fatigue.
Turbine Engine
Davenport, Otha B. (2001), Personal conversation, 29 June 2001, Groover, Charles Leonard (2000). Signal Component Removal Applied to the Order Content in Rotating Machinery, Master of Science in Mechanical Engineering Thesis, Penn State University. Hernandez, W., Paul, D., and Vosburgh, F (1996). On-Line Measurement and Tracking of Turbine Torsional Vibration Resonances using a New Encoder Based Rotational Vibration Method (RVM). SAE Technical Paper 961306, Presented at the Aerospace Atlantic Conference Maynard, K. P., and Trethewey, M. W. (2000), Blade and Shaft Crack detection Using Torsional Vibration Measurements Part 1: Feasibility Studies. Noise and Vibration Worldwide 31:11, 9-15. Maynard, K. P., and Trethewey, M, W. (2001) Blade and Shaft Crack detection Using Torsional Vibration Measurements Part 2: Resampling to Improve Effective Dynamic Range, Noise and Vibration Worldwide, 32:2, 23-26. Maynard, K. P., and Trethewey, M, (1999). On The Feasibility of Blade Crack Detection Through Torsional Vibration Measurements. Proceedings of the 53''^ Meeting of the Society for Machinery Failure Prevention Technology, 451-459. Maynard, K. P.; Lebold, M.; Groover, C ; Trethewey, M. (2000). Application of Double Resampling to Shaft Torsional Vibration Measurement for the Detection of Blade Natural Frequencies. Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology^, 87-94.
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McDonald, D, and Gribler, M. (1999). Digital Resampling: A Viable Alternative for Order Domain Measurements of Rotating Machinery. Proceedings of the 0^ Annual International Modal Analysis Conference. Parti, 1270-1275. Potter, R. (1990). A New Order Tracking Method for Rotating Machinery," Sound and Vibration, BOSS. Sawyer, John W., Ed. (1980). Sawyer's Turbomachinery Maintenance Handbook, 1st Ed., Vol. II, Turbomachinery International Publications, 7-34ff. Vance, John M. (1988). Rotordynamics of Turbomachinery, John Wiley & Sons, 377ff
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE FLOW-INDUCED VIBRATION OF CYLINDERS IN HEAT EXCHANGER W.Takano\ ICTozawa^ M.Yokoi^ M.Nakai ^and LSakamoto^ ^ Department of Precision Engineering, Kyoto University, Yoshida-honmach, Sakyo-ku, Kyoto 606-8501, ,JAPAN ^Junior College, Osaka Sangyo University, Daito, Osaka 574-8530, JAPAN ^Department of Mechanical Engineering for Transportation, Osaka Sangyo University, Daito, Osaka 574-8530, JAPAN
ABSTRACT Flow induced vibrations occur in general heat exchangers and nuclear reactors. They cause the tube failures through fretting wear or fatigue. Fluid elastic instability may lead to vibration amplitudes laige enough to cause tube-to-tube clashing and in such cases will lead to relatively rapid failures. Therefore mechanisms of elastic vibration induced by cross flow are significant problems for large vibration amplitudes. The aims of this study are to analyze the vibration of cylinders at various flow velocities and to get the fundamental data of design for the prevention of accident in heat exchanger. hi ftas study, the experimental apparatus was built so that cylinder tip motions could be measured at various flow velocities. The experiments were conducted in different cylinder arrays. The cylinder vibration was induced in normal direction to flow by an alternate vortex. Moreover, once the flow velocity was increased to a certain value, the cylinder oscillation amplitude increased rapidly with flow and had a maximum. When five cylinders were arranged in a row normal to flow, the wandering of energy was generated between the two cylinders. For three cylinders arranged in tandem, two cylinders in upstream and downstream positions mutually vibrated out of phase.
KEYWORDS Nonlinear Vibration, Nuclear Reactor, Altemate Vortex, Flow Liduced Vibration, Wandering of Energy
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INTRODUCTION Recently general heat exchangers and nuclear reactors are designed with each tube very close. At the same time, higher mass flow rate is needed to achieve higher eflQciencies of heat exchangers. But it is reported that vibration of tube bundles leads to tube failures asflowvelocity increases. Various tube failures in the nuclear industries have been given in the survey p^er by Paidoussis[I]. Therefore mechanisms of flow induced vibrations are significant problons. Moreover they have occurred in agricultural crop-wind interactions[2] and ciliated insideftroatsin biomechanics. In this study, the experimental ^paratus was built so that cylinder tip motions could be measured at various flow velocities. Thefimdamentaldata for cylinder motions at variousflowvelocities were presented and the mechanisms offlowinduced vibration were analyzed for prevention of accidents in heat exchangers.
EXPERIMENTAL FACILITY AND PROCEDURE The water channel facility, shown schematically in Fig.l, was designed and built for this study. It is about 250cm long, 100cm wide and 250cm high. Water in the lower reservoir © is dehvered to the iqjper reservoir ® using the line pump. Water in the reservoir © circulates throu^ the chamber, the working section and the reservoirs. The uniform flow velocity is provided by adjusting height of water in the reservoirs ® and opening of a valve located at the entrance of the chamber. Figure 2 shows the woiking section, which is 5mm wide by 71mm high andfebricatedfix)mPerspex (trade name, a kind of acrylic resin ). It is fixed to the chamber witii four bolts and connected to reservoir © by the pipe of diameter 20mm- Two kinds of thin cylinders of length 70mm were used instead of tubes: 0.5mm and 0.6mm in diameter. The ends of the cylinders arefixedto the base plate and the cylinders are regarded as cantilevers. The flow velocity around cylinders is indirectly calculatedfix)mthe flow sectional area of the woricing section by measuring the time required to deliver a liter water to the reservoir ©. A pair of strain gauges was fixed close to the root of flie cantilever cylinders so that the cylinder motions in orthogonal direction (streamwise and transverse direction) could be monitored. The stain gauges were calibrated and output
Reservoir (2) FFT analyzer Low pass filte|r Reservoir (2)
Water flow
^Strain gauges Fig. I Schematic diagram of water channel
Fig.2 Working section
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voltage from the strain gauge amplifier was found to vary linearly with the cylinder tip deflectioa Thus cylinder tip deflection could be determined from the output voltage. Since its amplitude was very small, unnecessary high frequency components were removedfix)moutput signals through the low pass filter. Frequency analyses for these obtained data were performed by the FFT analyzer. Moreover when the data for several cylinders were required at the same time, cylinder deflections were recorded by the data recorder. In this way reUable cylinder response data could be obtained. As Fig.3 illustrates, visualization experiments of cylinder motion and the interstitialflowaround cylinders were conducted The flow was illuminated along perpendicular path from the side via the halogen lamp, which produced the white tight. Thetighttraveled in straight lines through the slit so that it could not be scattered by Perspex of working section and water. Theflowwas visualized via blue ink, wltich was injected into theflowfromthe streamlined upstream. The photographic records of theflowwere taken from above the cylinder using a camera with the shutter speed as small as 1/1000s and high sensitivefilmsASA 1600.
EXPERIMENTAL RESULTS (1) Single cylinder: Vibrational mechanisms of cylinders in crossflowfall into various categories, such as turbulent buffeting, vorticity excitation andfluid-elasticvibration. At first the more detailed experiments for single oscillating cylinder in uniform waterflowwere carried out and theflowwas visualized so that the vibration mechanism could be made clear. In this study, non-dimensional reducedflowvelocity u, = Upl fd, reduced amplitude ^ = A/ d and reduced dumping coefficient c„ = mS/ (pd^) were employed asflowvelocity, amplitude and dumping coefficient, where u^: flow velocity, / : natural frequency of a cylinder in quiescent water , d : cylinder diameter, A : amplitude, m : mass per unit length of a cylinder, 8: logarithmic decrement of damping, p : density of water. Figure 4 shows the motions of a cylinder at variousflowvelocities. X and Y axes represent the transverse and streamwise cylinder deflections respectively. From this figure, it can be seen that the transverse deflection exceeds the streamwise one. The cylinder deflections | are plotted against the flow velocity U, for damping coefficients c„ =0.98,1.05 and 1.98 in Fig.5. Contrary to expectation, the oscillation was not observed at small flow velocity. As the flow velocity is incremented, the amplitudes abmptiy start to grow at a certain velocity. When theflowvelocity increasesfiirther,the amplitude decreases. Theflowwas visualized in order to examine the mechanism of such phenomena tliat the transverse amplitude was larger and the cylinder started to be stationary at a certainflowvelocity. Figures 6 (a) and (b) illustrate the flow
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Fig. 5 Amplitude versus flow velocity
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around oscillating and stationary cylinders, respectively. For a oscillation cylinder, an alternate vortex could be observed at the side face of cylinder. However it could not be observed for a stationary cylinder. When an alternate vortex occurs, the flow exfoliationfiiomthe surface of cylinder leads to the decrease of pressure. Then the cylinder transversely vibrates. Additionallyfrequencyof altemate vortex shedding is in resonance with a naturalfrequencyof a cylinder in water at theflowvelocity where the oscillating cylinder amplitude is the largest (2) Five cylinders arranged in a row normal to flow: Five cylinders are arranged in a row normal to flow with pitch-to-diameter ratio of 2.0 as shown in Fig.7(a). The strain gauges were fixed to the roots of the center cylinder C and its adjacent cylinder S. Figure 8 shows the amplitude responses of the cylinders C and S againstflowvelocity u,. Cylinders do not oscillate at a smallflowvelocity. The cylinder oscillating amplitudes increase rapidly at theflowvelocity of about 3.9. The amplitudes have the maximum at the flow velocity of 5.4, and decrease with the fiirther increase offlowvelocity. For the cylinders C and S, Fig.9 represents time histories andfrequencyspectra of cylinder deflection atflowvelocities of 4.0,6.2 and 7.9. Thisfigureshows that when the amplitude of the cylinder C is large, the amplitude of the adjacent cylinder S is small and vice versa. That is, the wandering of energy is generated between the two cylinders. This suggests that adjacent cylinders interact each otiier and
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fluid-elastic vibration occurs. (3) Three cylinders arranged in tandem: Figure 7 (b) shows three cylinders arranged in tandem. The strain gauges werefixedto the roots of two cylinders in upstream and downstream positions. Figure 10 shows the amplitudes e, of two cylinders versus theflowvelocity u,. Asflowvelocity increases, the cylinders start to vibrate at theflowvelocity of about 3.5. The amplitudes become larger as flow velocity is incremented. Both cylinders reached the maximum deflection at the flow velocity of about 6.5. For the cylinders C and D, Fig. 11 represents time histories andfi^quencyspectra of cylinder deflections atflowvelocities of 3.7, 5.2 and 6.5 as shown with arrows in Fig. 10. Though the wandering of energy is observed in the above mentioned experiment (Fig. 9), (a) u, = 4.o
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