NDT Data Fusion

NDT Data Fusion

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NDT Data Fusion X.E. Gros DUT, BSc (Hon), MSc, PhD Independent NDT Centre, France

A member of the Hodder Headline Group LONDON • SYDNEY • AUCKLAND Copublished in North, Central and South America by John Wiley & Sons, Inc., New York • Toronto

First published in Great Britain in 1997 by Arnold, a member of the Hodder Headline Group, 338 Euston Road, London NW1 3BH Copublished in North, Central and South America by John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012 ©1997XEGros All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronically or mechanically, including photocopying, recording or any information storage or retrieval system, without either prior permission in writing from the publisher or a licence permitting restricted copying. In the United Kingdom such licences are issued by the Copyright Licensing Agency: 90 Tottenham Court Road, London WIP 9HE. Whilst the advice and information in this book is believed to be true and accurate at the date of going to press, neither the author nor the publisher can accept any legal responsibility or liability for any errors or omissions that may be made. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN 0 340 67648 5 ISBN 0 470 23724 4 (Wiley) Typeset in 10/12 pt Times by Mathematical Composition Setters Ltd, Salisbury, Wiltshire SP3 4UF. Printed and bound in Great Britain by St. Edmundsbury Press, Bury St. Edmunds, Suffolk and Hartnolls Ltd, Bodmin, Cornwall.

To Rachael

Contents

Preface Acknowledgements List of Abbreviations 1

2

Introduction

1

1.1 1.2

1 2

4

Introduction In brief

Data Fusion - A Review 2.1 2.2 2.3 2.4 2.5 2.6

3

ix xi xiii

Introduction Data fusion system models Fusion methodology Data integration and fusion applications A practical example of NDT data fusion Conclusion

5 5 6 22 34 34 36

Non-destructive Testing Techniques

43

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12

43 44 46 48 51 57 58 59 66 71 72 73

Introduction Visual inspection Liquid penetrant inspection Magnetic particle inspection Eddy current testing Alternating current potential drop Alternating current field measurement Ultrasonic testing Radiographic inspection Additional NDT methods Computers in NDT Performance assessment of NDT methods

Scientific Visualisation

82

4.1 4.2 4.3 4.4

82 83 87 88

Introduction Data visualisation Volume visualisation Animation and virtual reality

viii

Contents 4.5 4.6 4.7

Fundamentals of image processing Visualisation in NDT Summary

5 A Bayesian Statistical Inference Approach to the Non-destructive Inspection of Composite Materials 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8

Introduction Composite materials Current NDT methods for the inspection of composites Description of test specimens Methodology and experimental design Inspection results NDT data integration and fusion Discussion

6 Application of NDT Data Fusion to Weld Inspection 6.1 6.2 6.3 6.4 6.5 7

Introduction Weld samples Non-destructive examination of the test specimens NDT data fusion Discussion

89 91 91

95 95 96 97 101 104 104 114 121 127 127 128 129 141 177

Perspectives of NDT Data Fusion

180

7.1 7.2

180 184

Concluding comments The future of NDT data fusion

Glossary Bibliography Index

189 195

Preface

This book is the first to be devoted exclusively to the concept of multisensor integration and data fusion applied to non-destructive testing (NDT). Data fusion is a rapidly evolving technology, and is now the most recent addition to NDT signal processing methodologies for efficient understanding and interpretation of data. This text provides a valuable source of information on NDT, data fusion, composite inspection, scientific visualisation and performance analysis of NDT methods. The text is intended for inspectors, students and researchers working in the fields of NDT, signal processing and measurement and testing, and delivers a comprehensive, easy-to-read guide on the concept of NDT data fusion. Its main objective is to initiate the readers into the subject by introducing data fusion processes. Problems are approached progressively through detailed, original experimental case studies, and solutions gradually implemented so that beginners can follow the procedures effectively. In addition, the oil, nuclear and aerospace industries may find the application of data fusion to weld and composite inspections worthwhile. NDT Data Fusion offers practical guidance for those wishing to develop and explore NDT data fusion further. It is intended to offer the most comprehensive introduction available at this time to the concepts of NDT and multisensor data fusion. In order to maintain a balance between theoretical and experimental discussions, chapters 1 and 4 present the theory behind NDT data fusion while chapters 5 to 7 concentrate on the implementation phase. Multisensor NDT data fusion is introduced in the first chapter which gives a general idea to the readers of the applications of non-destructive examination (NDE), its limitations and how data fusion techniques can contribute to the improvement of the overall performance of a non-destructive inspection. Chapter 2 presents a review of multisensor data fusion models, statistical and probabilistic methods currently used to combine information from multiple sources. A survey of data integration and fusion applications as well as a practical example of NDT data fusion are discussed. Existing non-destructive testing techniques, their advantages and limitations, are outlined in chapter 3. This chapter offers synthesised information about NDT techniques as well as their possible industrial applications. The computer visualisation of scientific data constitutes the topic of the fourth chapter. A brief description of the tools available and the applications of scientific visualisation in industry, and more particularly in NDT, is presented and illustrated. Virtual reality, computer animation and image processing techniques are also addressed. The first implementation of multisensor NDT data fusion in the assessment of composite materials using a Bayesian statistical inference approach is discussed in chapter 5. Inspection of composite samples currently used in the aerospace industry using multiple NDT techniques is described. The experimental results of these inspections are presented and the

x Preface performance of these techniques evaluated using probabilistic and statistical processes. The integration and combination of information from multiply eddy current sensors, using a Bayesian approach, and the outcome of this implementation, are discussed and analysed. Chapter 6 investigates NDT data fusion applied to weld inspection. The applications of data fusion highlighted in this chapter are directly relevant to nuclear and offshore industries. A Bayesian statistical inference approach and the Dempster-Shafer theory of evidence are used to combine data from multiple NDT instruments; their advantages and limitations are discussed in this chapter. Finally, chapter 7 concludes this book with discussion and comments on the current and future trends of NDT data fusion and its impact on industry. A bibliography of text and literature related to NDT, data fusion, visualisation and artificial intelligence is also included as well as a glossary of some of the most important terms used in these disciplines. It is hoped that NDT Data Fusion will give the reader a general overview of what is still required and what could be achieved to maintain high safety levels in industry, and will provide engineers, researchers, students and NDT inspectors with a more complete picture and a more accurate assessment of structural integrity than are currently possible with a single NDT technique. Finally, it is hoped that readers will gain an understanding of the great benefits which can be achieved by implementing multisensor data fusion, not only in NDT but in any discipline involved in measurement and testing. X.E. Gros

Acknowledgements

The author would like to thank P. Strachan and D.W. Lowden from the Robert Gordon University, Aberdeen, for the useful advice and support they provided throughout his research work on multiprobe NDT data fusion. The author also wishes to acknowledge G. MacGregor (Core Technical) for his interest in this research and for the loan of NDT equipment without which measurements could not have been carried out. Special thanks are also due to D. Graham and to I. Findlay for access to radiographic and infrared thermographic equipment. In addition, the author would like to acknowledge J. Bousigue (Ingenieur E.N.S.I. de Caen, D.E.A. Traitement et Synthese d'Image) for his invaluable discussion and generous help in programming. Last but not least, the author cheerfully thanks Dr R.D. Wakefield for her faithful support, patience and encouragement during completion of this book.

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List of Abbreviations

These acronyms and abbreviations are official terms related to research and NDT in Europe. They are widely used in present-day papers and NDT literature. This list is not definitive and is given only for information as some of the terms are used in this book. AC ACFM ACPD ADC AE AGOCG AI apE A-Scan ASCII AVS Bel bpa BS B-Scan BVID CAD CCD CIE C3I CCTV CMYK

Alternating Current Alternating Current Field Measurement Alternating Current Potential Drop Analog-to-digital Converter Acoustic Emission Advisory Group on Computer Graphics Artificial Intelligence Animation Production Environment Amplitude Scan American Standard Code for Information Interchange Application Visual Software Belief Basic Probability Assignment British Standard Brightness Scan Barely Visible Impact Damage Computer Aided Design Charge Coupled Device Commission Internationale de l'Eclairage Command Control Communication and Intelligence Closed Circuit Television Cyan - Magenta- Yellow Black

Crack Opening Displacement Central Processing Unit Cathode Ray Oscilloscope Cathode Ray Tube Contrast Scan Computerised Tomography Digital-to-analog Converter Decibel Direct Current Direct Current Potential Drop Distance-Gain-Size Deep Penetration Eddy Currents Eddy Current EC Electromagnetic Array EMA Electromagnetic Acoustic EMAT Transducer emf Electromotive Force Expert System ES ET/ECT Eddy Current Testing Focus-to-film Distance FFD Fast Fourier Transform FFT GEP Generalised Evidence Processing Glass Fibre Reinforced GFRP Plastic GMAW Gas Metal Arc Welding GTAW Gas Tungsten Arc Welding HAZ Heat Affected Zone He-Ne Helium-Neon HSV Hue-Saturation-Value HVT Half Value Thickness

COD CPU CRO CRT C-Scan CT DAC dB DC DCPD DGS DPEC

xiv List of Abbreviations IACS ID IEEE IIT IRT IT IQI KBS LCD LOSWF LPI LR MPI MPT MRI MRF NDA NDE NDI NDT NMR NN NP OD OP PC PCB PFC PISC Pis

International Annealed Copper Standard Inner Diameter Institute of Electrical and Electronics Engineers Image Intensifier Tube Infrared Thermography Information Technology Image Quality Indicator Knowledge Based System Liquid Crystal Detector Lack of Side Wall Fusion Liquid Penetrant Inspection Likelihood Ratio Magnetic Particle Inspection Magnetic Particle Testing Magnetic Resonance Imaging Markov Random Field Non-destructive Assessment Non-destructive Examination Non-destructive Inspection Non-destructive Testing Nuclear Magnetic Resonance Neural Network Neyman-Pearson Outer Diameter Operational Amplifier Personal Computer Printed Circuit Board Probability of False Calls Programme on the Inspection of Steel Components Plausibility

POD P-Scan PT QA QC QNDE RFEC RGB ROC ROV RT RTR SAFT SAW

sec SEM SFD SNR SP SQUID 3-D TOFD TV UT UV VDA VDU VT XR

Probability of Detection Projection Scan Penetrant Testing Quality Assurance Quality Control Quantitative Non-destructive Evaluation Remote Field Eddy Current Red-Green-Blue Receiver (or Reliability) Operating Characteristic Remotely Operated Vehicle Radiographic Testing Real-time Radiography Synthetic Aperture Focusing Technique Surface Acoustic Wave Stress Corrosion Cracking Scanning Electron Microscopy Source-to-film Distance Signal-to-noise Ratio Signal Processing Superconducting Quantum Interference Device Three-dimensional Time-of-flight Diffraction Television Ultrasonic Testing Ultraviolet Visual Data Analysis Visual Display Unit Visual Testing X-ray Radiography

1

Introduction Science is nothing but trained and organised common sense T.H. Huxley, 1878

1.1

Introduction

In order to improve manufacturing quality and ensure public safety, components and structures are regularly inspected for defects or faults which may reduce their structural integrity. Among the methods of testing developed for maintenance and inspection purposes, non-destructive testing (NDT) techniques present the advantages of leaving the components undamaged after inspection. Such techniques find applications in the aerospace,1'2 transport,3'4 nuclear,5'6 food7 and offshore industries.8'9 Most NDT methods can now be automated and computer controlled in order to facilitate signal interpretation.1011 Despite these improvements, non-destructive examinations (NDE) are usually performed by a qualified NDT inspector using NDT techniques which are applied on an individual basis. Scientific measurements based on a single sensor can provide only limited information about the environment in which it operates. Because each NDT method presents different advantages and limitations, the use of more than one method is usually required to inspect a material fully. For example, ultrasonic testing helps in the detection of internal defects while eddy current examination is more appropriately applied in the detection of surface breaking defects. However, information from different NDT systems can be conflicting, incomplete or vague if looked at as discrete data. The concept of data fusion can be used to combine information from multiple NDT systems and help in decision making to reduce human error interpretation. Data fusion can be defined as the synergistic use of information from multiple sources in order to assist in the overall understanding of a phenomenon. Multisensor data integration and fusion have gained popularity in military and robotics applications and more recently in non-destructive testing; data fusion applied to NDT was first introduced in 199312 and research interests are rapidly increasing throughout Europe.13"15 During the last decade, although considerable research effort has gone into the application of data fusion to robotics,1617 imaging techniques18 and target tracking,19,20 relatively little use has been made of the concept in NDT. Thus the objective of this text is to present recent research advances of interest to the NDT community by taking the existing base of published knowledge and adapting and extending this into a model which can be used to enhance the value and cost-effectiveness of non-destructive methods of testing, analysis and evaluation.

2 Introduction The development of a data fusion process to combine information from multiple nondestructive testing sensors in order to provide a more complete picture and a more accurate assessment of structural integrity than is currently possible with a single NDT method is described in this book. A review of the existing multisensor data integration and fusion models, methods and applications is discussed in chapter 2, to help determine and understand the data fusion processes to be used in the implementation phase. The actual NDT techniques available, their advantages and limitations, are described in the third chapter, while chapter 4 presents an introduction to scientific visualisation and identifies the usefulness of visualisation methods to present NDT data efficiently. NDT data fusion, implemented through two different approaches, is analysed in chapters 5 and 6; first, a Bayesian statistical approach was used to make inference and test binary hypothesis from information collected from multiple eddy current sensors used to inspect composite materials. This approach demonstrated the potential of Bayesian theory, and visualisation enabled data to be presented in a colour-coded visual format. In addition, the efficiency of different NDT techniques used in the inspection of composite materials was analysed using statistical theories which are described in this book. The second data fusion approach concerned the inspection of welds. The procedures carried out can be of direct relevance to the nuclear and offshore industries which are currently inspecting welds using more than one NDT method. A Bayesian statistical inference approach and the Dempster-Shafer theory of evidence were used to combine information from (i) multiple NDT sensors of a similar type but from different instruments, and (ii) different NDT sensors. A study was made of the performance and efficiency of each approach to combine data effectively and to provide the user with valuable results, and the most appropriate approach for NDT data fusion was identified. This book concludes with personal views regarding the future of NDT data fusion, its implications in industry and how it can be developed further. 1.2

In brief

Different probability approaches to NDT data fusion were studied and their efficiency in combining information was assessed using statistical theories such as probability of detection21 and receiver operating characteristic curves.22 The NDT data fusion process implemented presents results in the form of probability associated with a measurement which is used to make inference.23 Because analogue signals on cathode ray tubes are difficult to analyse, a data visualisation approach was adopted to facilitate signal interpretation and provide the user with qualitative and quantitative information about defects. This information is very useful to the structural engineer who needs to advise on possible levels of failure of a component. Visualisation of NDT data and fusion of information at pixel level were also performed, as described in this.book. References 1. Hobbs C, Temple A. The inspection of aerospace structures using transient thermography, April 1993, The British Journal of Non Destructive Testing, 35(4), 183-9. 2. Wassel AB. Safety and reliability in the air, June 1993, The British Journal of Non Destructive Testing, 35(6), 315-18.

References 3. 4. 5. 6. 7. 8. 9. 10.

11.

12. 13.

14.

15. 16.

17.

18.

19.

20.

21.

3

Egelkraut K. Are the railways the real pioneers of NDT?, May 1994, Insight, 36(5), 306-9. Gartside C. Automated ultrasonic testing of rail axles on TGV and Channel Tunnel trains, May 1994, Insight, 36(5), 310-12. Rylander L, Gustafsson J. Non-destructive examination of the primary system in Igulania nuclear power plant, April 1994, Insight, 36(4), 210-12. Gartside C, Hurst J. Application of TOFD inspection technique to fasteners in power generating plant, April 1994, Insight, 36(4), 215-17. Lacey RE, Payne FA. Ultrasonic velocity in used corn oil as a measure of quality, 1994, ASAE Transactions, 37(5), 1583-9. Rogers LM. Sizing fatigue cracks in offshore structures by the acoustic emission method, Sept. 1994, Insight, 36(9), 6 6 1 - 5 . Raine GA. An alternative method for offshore inspection, Sept. 1994, Insight, 36(9), 678-82. McNab A, Dunlop I. Advanced visualisation and interpretation techniques for the evaluation of ultrasonic data: the NDT workbench, May 1993, The British Journal of Non Destructive Testing, 35(5), 233-40. Smith RA. Evaluation and accuracy assessment of Andscan - a portable nondestructive scanner - Part 1: Andscan hardware and software, April 1995, Insight, 37(4), 284-9. Edwards I, Gros XE, Lowden DW, Strachan P. Fusion of NDT data, Dec. 1993, British Journal of Non Destructive Testing, 35(12), 710-13. Georgel B, Lavayssiere B. Fusion de donnees: un nouveau concept en CND, 24-28 Oct. 1994, Proceedings of the 6th European Conference on Non Destructive Testing, Nice, France, 1, 3 1 - 5 . Johannsen K, Heine S, Nockemann C. New data fusion techniques for the reliability enhancement of NDT, 24-28 Oct. 1994, Proceedings of the 6th European Conference on Non Destructive Testing, Nice, France, 1, 361-5. Gros XE, Strachan P, Lowden DW. A Bayesian approach to NDT data fusion, May 1995, Insight, 37(5), 363-7. Carrol MS, Meng M, Cadwallender WK. Fusion of ultrasonic and infrared signatures for personnel detection by a mobile robot, Proceedings of the SPIE Conference, 1611, Sensor Fusion IV, 1991,619-29. Mandelbaum R, Mintz M. Active sensor fusion for mobile robot exploration and navigation, Proceedings of the SPIE Conference, 2059, Sensor Fusion VI, Sept. 1993, 120-9. Pinz AJ, Bartl R. Information fusion in image understanding: Landsat classification and Ocular Fundus images, Proceedings of the SPIE Conference, 1828, Sensor Fusion V, Nov. 1992, 276-87. Stewart L, McCarty P. The use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking and situation assessment, Proceedings of the SPIE Conference, 1699, Signal Processing, Sensor Fusion and Target Recognition, 1992, 177-85. Thomopoulos SCA, Chen BH. Fusion of Ladar and FLIR data for enhanced automatic target recognition, Proceedings of the SPIE Conference, 2093, Substance Identification Analytics, Oct. 1993, 600-9. Berens AP, Hovey PW. Evaluation of NDE reliability characterization, Dec. 1981, AFWAL-TR-81-4160, Vol. 1, Air Force Wright-Paterson Aeronautical Laboratories.

4 Introduction 22. Centor RM, Keightley GE. Receiver operating characteristics (ROC) curve area analysis using the ROC Analyzer, Nov. 1989, Proceedings of the 13th Symposium on Computer Applications in Medical Care, IEEE Publications, 222-6. 23. Gros XE, Strachan P, Lowden D. Theory and implementation of NDT data fusion, 1995, Research in Non Destructive Evaluation, 6(4), 227-36.

2

Data Fusion - A Review Data fusion is deceptively simple in concept but enormously complex in implementation US Department of Defense, 1990

2.1

Introduction

Scientific measurements using identical or disparate multiple sensors generate large amounts of data of similar or different class which need to be processed in a meaningful way. Owing to the increasing demand for more accurate information, a practical and robust procedure needed to be developed to manage data efficiently, in order to improve system reliability. The systematic integration of multisensor information is known as data fusion. Its aim is to combine and manage multisensory data (by integrating information from multiple sources) in order to obtain a more complete evaluation of the environment in which the sensors operate. Multisensor data integration and fusion can be described as the synergistic use of information from multiple sources to assist in the overall understanding of a phenomenon and to measure evidence or combine decisions. The data fusion concept is not a new process; it appears in scientific literature of the late 1960s1,2 in a theoretical form using mathematical algorithms before being implemented in the 1970s and 1980s in multiple disciplines.3"5 In 1984 a data fusion sub-panel was established in the USA, organising conferences, promoting data fusion to industry and educational establishments, identifying the needs of industry and coordinating data fusion projects. From a survey of publications related to data fusion, it has been estimated that worldwide more than 50 universities and industrial companies are currently performing (or have carried out) research in multisensor data fusion and integration. When it first appeared in the literature, the term 'data fusion' had no particular meaning for many scientists. Only an industry in need of an efficient and operator-independent data management and analysis system, with substantial capital, was able to invest and begin research in this field. For these reasons, most of the early data fusion projects were military oriented, their principal objectives being to improve the efficiency of national defence by developing a man-machine interface system. This would reduce the skills required for data interpretation and decision making operations for battlefield surveillance or tactical situation assessment. Therefore the majority of the publications relating to data fusion have been focused on defence applications where its potential was realised.6"11 Nowadays the areas of application of data fusion span a broad range of disciplines such as robotics,12 nondestructive evaluation,13 pattern recognition,3 geoscience,14 medicine15 and even finance.16

6 Data fusion - a review This chapter gives a broad overview of what has been achieved to date and shows the potential of the technique by presenting a resume of several data fusion implementation areas. A survey of the most frequent data fusion systems together with the advantages and limitations of some of these techniques is also included. The last section describes a practical implementation of data fusion applied to non-destructive evaluation. 2.2

Data Fusion System Models

This section describes multiple data fusion models and compares centralised and distributed fusion systems. The role of data fusion, as viewed through several publications, and its use in managing uncertainty and improving accuracy are presented. The features that characterise multiple sensor devices as opposed to single sensor devices are briefly reviewed. 2.2.1 FROM BIOLOGY TO TECHNOLOGY The adaptation of data fusion to technology appeared through artificial intelligence, the theory of which is based on the development of an artificial system which will be able to reproduce human reasoning. Data fusion is carried out by the human brain when, for example, associating images and sound while watching television. Some interesting examples of human and other animal data fusion processes are discussed by Luo and Kay17 whose paper presents a fundamental review of data fusion. Pearson et al.x% present an analogy between the neural system of barn owls to detect and locate prey, and an artificial neural network for target tracking. A barn owl performs fusion of visual and acoustic information via a series of four computational maps whose combined information is used for target localisation. Using a similar artificial neural network system, these authors produced an efficient target location mechanism which could be adapted for technical applications. The fusion of multiple information by humans occurs every time the senses are stimulated by appropriate signals. Our sensors, for example eyes, ears, nose, tongue and skin, are fusing sight, hearing, smell, taste and tactile information in our brain. The sound of a voice combined with visual information helps in identifying a person; Fig. 2.1 illustrates this example. The human brain is probably the best analogy to a data fusion system. Artificial intelligence (AI) is a science which is trying to reproduce the advantages of human behaviour, such as reasoning, identification or combination of information, in a more technological way in order to minimise human error.19 Rauch20 presents aspects of tactical data fusion using expert systems for decision making. Artificial intelligence systems still need more research, and significant progress will have to be made before they are able to compete with the human fusion centre on an autonomous and consistent basis. The scientific reality is not as simple as it appears; several parameters have to be taken into account by an artificial system in order to perform efficient integration of information. The quality and accuracy of the sensors, the environment in which they operate and the type of information collected are all factors which may influence the quality of a fusion system if they have not been clearly specified and adequately assessed. 2.2.2 MULTIPLE VERSUS SINGLE SENSOR DEVICES Sensors are devices used to obtain information from the environment in which they operate. For example, radar in aerospace applications is used to detect the presence of a

Data fusion system models Sensor 1

1

Sensor 2

Fig. 2.1 Illustration of the human data fusion system vehicle, underwater sonar gives information on the range of an object, and an ultrasonic sensor in non-destructive examination aids in the location of an internal flaw in a component. Information from a single sensor can be very limited; in the previous examples, radar information would be more complete if the vehicle could be identified, and it would be more valuable if the shape of objects detected by the sonar could be elucidated. In non-destructive evaluation, it is essential to detect surface flaws as well as internal ones. Measurements taken using single sources are not fully reliable and are very often incomplete due to the operating range and limitations which characterise each sensor. The transmission of information is also dependent on the reliability of the system. Moreover, the electronics of the sensor, the operational environment (underwater, space) or natural phenomena such as storm and lightning can affect sensor reading by reducing the signal-tonoise ratio. The use of multiple sensors has numerous advantages over single sensor instruments. Because of the technical features which characterise each sensor, redundant and/or complementary observations about a measurand are made. The combination of this information can be used to generate a more complete picture of the environment than is currently obtainable with a single sensor. Using the previous examples, a satellite image could be coupled with radar information, an infrared camera added to a sonar and an eddy current probe combined with an ultrasonic sensor, to enable the missing information, in this case vehicle identification, depth information and flaw information, to be gained. A multiple sensor device can include any instrument with several sensors of identical or dissimilar types used to measure a physical quantity. The simultaneous use of similar sensors can be very advantageous when large areas need to be covered in a short time, or to assess the accuracy of a reading by comparing multiple outputs. Magee and Aggarwal21 and Krzysztofuwicz and Long22 presented the advantages of using multiple sensors over a single sensor using several examples, and Smith et alP defined an algorithm to detect faults in multisensor probes. Dawn24 presented some fundamental limits in multisensor

8 Data fusion - a review data fusion, and Chao et al.15 demonstrated that increasing the number of sensors led to a significant reduction in error. The probability of error decreases asymptotically with the number of sensors (Fig. 2.2). However, increasing the number of sensors also increases the complexity of a system.

Number of sensors Fig. 2.2 Probability of error versus number of sensors

The following benefits can be identified in the use of multiple sensor devices: • •

a reduction in measurement time a downtime reduction and an increase in reliability redundant and complementary information a higher signal-to-noise ratio a reduction in measurand uncertainty a more complete picture of the environment.

All of these result in an overall increase in system performance. Information from multiple sources needs to be effectively combined in a coherent and efficient manner in order to compensate for their limitations and deficiencies. Several data fusion models have been developed which are presented in the next section. 2.2.3 SENSOR MANAGEMENT Data integration is a process which gathers sensor information and relates it to other information in order to identify common sources. Because sensor information can differ in time, space and accuracy, an efficient sensor management system must be considered. Sensor information in data fusion The decreasing cost of sensors has made possible the use of more sensors in science. Each sensor can produce either a single signal/decision or multiple signals (subset of

Data fusion system models 9 hypotheses). The data from any type of sensor can be fused providing that each sensor refers to the same measurand. Sensor selection is difficult and depends on the type of information required as well as the application. The use of both identical and different sensors presents advantages and disadvantages. With identical sensors, the signal output is in an identical format which can be fused with minimum processing and can be used to validate the information provided by a previous sensor. By comparing information from multiple identical sources, redundant information increases the certainty and reliability of an inspection. Lee and Van Vleet26 gave an estimate of the error between multiple identical sensors in order to improve the efficiency of a fusion system. Durrant-Whyte27 used multiple cameras to track objects. However, the use of identical sensors is limited, as they provide the same type of information and present similar strengths and weaknesses. The use of different sensors presents the advantage of providing complementary information which can be fused to enhance the overall performance of an inspection. In this case, the signals will have to be processed to obtain an identical data format. Identification of the sensors in operation has to be performed to process the data according to sensor type. The multisensor inputs must be related to one another in both time and space for real-time data fusion applications. In non-real-time applications, input data may be related only in space. Table 2.1 presents a survey of the different types of sensor which have been used in data fusion. Among the most commonly used sensors are radar, cameras and infrared.28 It is not surprising to find these sensors at the top of the list as they are widely used in military applications for surveillance and target tracking. Table 2.1 Survey of typical sensors used in data fusion Sensor

Output format

Applications

References

Optical sensor

Image

Mobile robot guidance

Radar

Pulse signal

Infrared sensor

Image

Satellite

Image

Target detection and target tracking Object identification Surveillance and pattern recognition Mobile robot guidance Materials examination Obstacle detection Pattern recognition Medical

1 8 , 2 6 , 2 7 , 3 1 , 3 4 , 35, 5 9 , 6 1 , 6 2 , 6 5 , 7 2 , 73, 77, 90, 100 10, 29, 30, 36, 42, 49, 72,85,94,96 1 0 , 2 6 , 2 9 , 3 1 , 3 6 , 72, 86,98 3 , 4 , 14,32,33

Ultrasonic sensor Pulse signal NDT sensor

Voltage

Sonar Laser X-ray

Pulse echo Image Image

37, 74, 53, 77,

34, 59, 60, 90 1 3 , 8 3 , 9 1 , 103 36, 37,53 21,60,80 15

Combining laser radar images with infrared images is very common for target detection.10'29"31 Johnson et al.32 and Ehlers33 gave examples of satellite image data fusion for missile location and weather broadcast applications. Franklin and Blodgett14 described the application of data fusion in the field of geoscience, by fusing multiple satellite images

10 Data fusion - a review used for classification, discrimination of vegetation communities, and analysis and mapping of ecological areas. Sensors are used because they help to enhance human senses or provide additional information otherwise unobtainable (e.g. sonar, radar). One of the major advantages of using multisensor systems is that information can be more accurate and more rapidly transferred. Robotics make great use of sensors for position location, distance assessment, object recognition and guidance. Richardson and Marsh34 and Shapiro and Mowforth35 combined vision, tactile and range sensors for industrial mobile robots to perform assembly tasks and classification. Flynn36 addressed the problem of combining information from sonar and infrared sensors for mobile robot navigation. A sonar gives range measurement as a dense sample of data but no depth information. Infrared sensors provide good depth information but poor distance measurement. Information from each sensor is integrated to overcome the deficiency of the other in order to produce a more accurate representation of the environment in which the robot operates.37 McCoy11 described an application of data fusion for fuel management requirements of military fighter aircraft. Sensor characteristics are a limiting factor in the performance of a data fusion system. Sensor performance The performance and potential of each sensor used needs to be established in order to assign weight of evidence, for example. The uncertainty of each of the sensors listed in Table 2.1 can be modelled as a Gaussian distribution; this will also be the case for NDT sensors. Sensor modelling is a very important characteristic for a multisensor fusion system using a Gaussian distribution. System accuracy is limited by the resolution, sensitivity and precision of sensors. Only by knowing the limit of each sensor will meaningful information with a high confidence level be extracted. Uncertainty and errors are other factors which may cause problems for signal interpretation and decision making. The most common sources of uncertainty are: • •

little or no knowledge about a measurement; incomplete measurement (when data are approximated rather than waiting for complete data which may be time-consuming and costly); • limitations of the system.

Sensor uncertainty can be a source of wrong decision making. In NDT, for example, uncertainty may mean unnecessary repair cost or structural failure if a major fault has been detected but wrongly identified and no action taken. Figure 2.3 shows some common types of errors. Sensor performance can be statistically represented using detection probability criteria. Such a criterion can be the probability of detection (POD) of a given measurand by a specific sensor. In NDT, POD curves are plotted against flaw size and are used to assess the potential and limitations of a technique.39 An idealised POD curve is a step function, but more realistic POD curves are not as perfect (Fig. 2.4). A system should be able to detect all flaws above a critical flaw size, denoted Cs9 in order to fulfil quality and safety standards. Sensor performance can also be expressed as a receiver operating characteristic (ROC) graph which plots the probability of detection versus the probability of false

Errors

Inspection practicality (environment)

Incorrect output

Unreliable Fig. 2.3 Common types of errors (modified from Giarratano and Riley 38 )

12 Data fusion - a review 1.0

g 0.5

0.0

0.0

Cs

0.5 Flaw Size

1.0

Fig. 2.4 Typical POD versusflawsize curve alarm (Fig. 2.5).40 Van Dijk and Boogaard41 expressed the performance of a system as £=l-2V(l-POD)PFC

(2.1)

where POD is the probability of detection and PFC the probability of false call.

0.0

Probability of false alarm Fig. 2.5 Typical ROC curve

For a fictitious system, the performance increases as k increases as shown in Fig. 2.5. ROC curves are very often used to compare the performance between two or more sensors where each set of sensor data is assumed to be statistically independent. The major advantage of ROC curves compared to POD curves is that false calls are taken into

Data fusion system models 13 account to plot a ROC curve; with a POD curve there is no information on the number of false calls of a system. In practice, ROC curves are difficult to realise. Evaluation and characterisation of the performance of a sensor have to be assessed prior to fusion and before applying a weight to a particular system. Parra-Loera et al.42 presented a methodology to determine sensor confidence factors for a multitarget tracking environment using the Dempster-Shafer mathematical theory of evidence. Blackman and Broida94 evaluated the performance of multiple sensor tracking systems in aerospace applications. The system can be very complex, especially if we update the weight as a function of different parameters which may influence a sensor (i.e. location, temperature, experimental setup). 2.2.4 DATA FUSION MODELS As the terminologies - data fusion, data integration, multisensor integration - become more widely used in day-to-day scientific publications, their meaning needs to be clarified. Waltz and Llinas,8 Hall9 and Rothman and Denton43 gave their views and definitions of what data fusion really is. In 1990, the US Department of Defense defined data fusion as 'a technology which involves the acquisition, integration, filtering, correlation and synthesis of useful data from diverse sources for the purposes of situation/environment assessment, planning, detecting, verifying, diagnosing problems, aiding tactical and strategic decisions, and improving systems performance and utility'. This is a very complex definition oriented towards military applications rather than a general explanation of data fusion. In simple terms it can be summarised as the processing, interpretation and use of data from multiple sources. Data fusion is used in an important variety of topics and technologies. A general data fusion system model capable of handling various applications is very difficult, if not impossible, to design. As a consequence, various data fusion models can be found in the literature. General reviews on data fusion were presented in 1988 by Blackman,44 Schoes and Castore45 and Luo and Kay,46 in 1990 by Hackett and Shah,47 and in 1991 by Rothman and Denton43 where different fusion technologies were described. Luo and Kay17 presented a detailed survey of multisensor integration and fusion systems. Their paper covers the broad aspects of data fusion with clear descriptions, examples and an extensive bibliography which is very useful to anyone requiring to know more about this topic. In Luo and Kay's model, the outputs of two sensors are fused into a new representation. This is then fused with information from a third sensor and so on. Fusion occurs gradually with care and deliberation using only two sets of information at a time. An interactive information system composed of three units - sensor selection, world model and data transformation - is used to modify the fusion process. The first unit, sensor selection, is used to select the most appropriate group of information from sensors to be fused. Sensory information is represented within the world model and the data transformation system is used to normalise data prior to fusion. A functional data fusion model for multisensor non-destructive testing (NDT) data is presented in Fig. 2.6. In the first phase, measurements from n sensors are integrated. At this stage the raw data are processed, using thresholding, averaging or image processing techniques, and converted to a common numerical format usable by the fusion unit where data association is performed. Evidential reasoning, probabilistic and belief theories are used to process the data further and make inferences. The results are classified and selected before a decision on the optimum fused sensor data information can be made. With this

14 Data fusion - a review system, information from multiple sensors is processed by the same data integration centre before being fused.

Sensor 1

Sensor 2

XI

Y1

X2

Y2 Data

Processing

, 1

Assignment Z2 ,

^ Processed

Rawd; dai

Zl

te

W

of Bayes or Dempster/Shafer Rule of

data

Combination

Sensor n

Z*

Yn

Xn

•

^ Integration

Fusion

Integration

Multisensor data integration and fusion centre

Fig. 2.6 Functional model for a data fusion process Krzysztofuwicz and Long22 outlined three schemes for fusing information in multisensor detection systems. The first scheme, fusion at the sensor level, fuses raw sensor data. The second one fuses decision information after data processing and integration. The final scheme fuses detection probabilities resulting from sensory data information. The performance of each scheme is assessed using a Bayes risk theory. The authors concluded that fusion of detection probabilities offers advantages over the other two schemes. Among these are higher performance and flexibility, and better suitability to situations where observations are in a probability format. They also described a probability fusion and decision model making use of likelihood and Bayesian statistics. A specific data fusion model can be created for each particular application but it sometimes appears that models may also vary within an identical application. Functional models of a data fusion process for target tracking and identification have been presented by Waltz and Llinas.8 Tong et al.29 and Ruck et a/.30 described different models for the same application. Another general approach to data fusion was proposed by Pau48 whose paper reviewed some knowledge representation approaches devoted to sensor fusion. Methodological problems in multisensor data fusion and application to fusion of acoustical and optical data are given by Richardson and Marsh.34 Data fusion models differ from author to author, but they all agree on a three-level process. Thomopoulos49 presented a sensor integration paradigm composed of three levels: the signal level, the level of evidence and the level of dynamics (Fig. 2.7). The integration phase at the signal level is called data fusion, at the level of evidence it is referred to as features fusion, and decision fusion relates to the level of dynamics. Artificial neural networks can be used at the signal level for image processing. Statistical models and probabilities are used to describe the experiment carried out and to integrate data at the evidential level. This level makes use of a Bayesian approach or Dempster-Shafer theory. A mathematical model that describes the experiment is used at the level of dynamics. Harris50 described a three-level distributed fusion system. Pau48 demonstrated the performance of a multilevel data fusion approach. His introduction will

Data fusion system models

15

Sensors y' Data Fusion

T

T

Signal Level

Features Fusion

Level of Dynamics

Level of Evidence

Decision Fusion

T

Fig. 2.7 A three-level fusion paradigm (modified from Thomopoulos49) certainly convert any non-believer in data fusion into a fervent follower of the theory. He also reported the benefits of combining evidence from multiple sensors (e.g. better feature, reduced cost, achievement of sensor diversity, increased speed, provision range data, etc.). The choice of the fusion level depends mainly upon the application and complexity of the system. An extensive survey of multisensor data fusion systems was presented by Linn et al.2S Their survey was oriented towards military applications of data fusion systems. For each of the 54 data fusion systems they identified, a short description and comments were given, providing the reader with detailed information on the performance of each system. Among the 54 data fusion systems identified, 49 were performing fusion of information at the first and second levels and only five at the third level. This may be due to the numerous sensors producing image-based data from satellites used by the military services and also to a decision to minimise the complexity of a system by performing most of the operations at the first level. Two of the most common types of architecture for data fusion systems are centralised and distributed (or decentralised) decision structures (Figs 2.8a and 2.8b respectively). Chao et al.,25 Schoes and Castore,45 Tenney and Sandell51 and Thomopoulos52 have compared their performances and presented decision theories for distributed systems. | Sensor 1 | — • Measurement — i

'

i

>

r v 1

Fusion

Decision w

1 Sensor 2 |—^* Measurement i i

Centre

i

i

1

i

i

1

| Sensor N|—>* Measurement —

Level

J

Fig. 2.8a Centralised signal detection system Distributed signal detection systems fused identity declarations using Bayesian theory, the Dempster-Shafer paradigm or Thomopoulos generalised evidence processing (GEP). The output from each sensor is a decision and these decisions form the inputs to a fusion centre where association is performed. Centralised systems are more suitable for the fusion of raw data but the association phase can be difficult. They are used with commensurate

16 Data fusion - a review Sensor 1

- • Measurement

- Local Decision Feature

Sensor 2

-•Measurement

Extraction

Local Decision

Level Sensor N

- • Measurement

- Local Decision

Fig. 2.8b Distributed (decentralised) signal detection system

sensor data (e.g. infrared images, satellite images) which are fused using pattern recognition or estimation techniques with high computational requirements. Chair and Varshney53 presented a data fusion structure for distributed detection systems. Distributed signal processing (SP) is attractive as the SP is performed at the sensor level, reducing cost and communication bandwidth and increasing reliability. Viswanathan et al.54 compared parallel and serial data fusion systems with statistically independent sensors. They proved the optimality of a Neyman-Pearson test when employed at multiple stages of the fusion process. Their implementation of the data fusion system concluded in favour of a parallel distributed fusion scheme. Thomopoulos and Okello55 presented a distributed fusion system for decision making between sets of information from two sensors. One sensor is used for final decision making, the other is used to consult and check the decision taken by the first one. This system is advantageous when there is little information on the prior probability. Multisensor systems can be categorised into four major network types: parallel, serial, parallel-serial and serial-parallel systems. A parallel sensor suite combines information in parallel as presented in Fig. 2.9, and is well suited to the fusion of measurand

Sensor 1

Sensor 2

Sensor j Fig. 2.9 Parallel multisensor suite

Data fusion system models 17 information from identical or dissimilar sensors. A serial sensor suite is more suited to sensors of different ranges which are complementary in a sequential order (Fig. 2.10). A serial sensor suite consists of j sensors in series from which the information is sequentially combined.

Sensor 1

Sensor 2

Sensor j

Fig. 2.10 Serial multisensor suite Sensor output can be regarded as a decision array of n decisions. The efficiency of each sensor, noted rjj9 is the probability of correctness of the decision Dy from sensor j ; it is a measure of the effectiveness of a sensor. The following is based on Dempster-Shafer theory which is described in section 2.3.3. Let us define C; and Wj as the belief that the decisions from sensor j are correct and wrong respectively. By definition, from the Dempster-Shafer theory we can write Cj+Wj^l and U}= 1 - (Cy+ Wj) where £/, is called the ignorance (or uncertainty) of a measurand. From Shafer56 and Dasarathy,57 CL+

WL+

UL=

UL

(2.2)

where ck9 wk and uk are the incremental probabilities of the fused correct and incorrect decisions and non-decision respectively at the kth stage of the fusion process. Recursively, we obtain A.

K

(2.3)

which can be written as Ck+Wk+uk=l

(2.4)

uk=uk_i-ck-wk

(2.5)

C, = c 1 ^ M ( /- 1 »

(2.6)

and with

1=1

it

wt-w^ur*

(2.7)

;=1

Therefore k

1

uk

(2.8) /-l

withO^Wi^l.

\-U\

18

Data fusion - a review

Ck and Wk are the summed correct and incorrect fused decision rates; equations (2.6) and (2.7) can be rewritten as 1

*

Ck = cx \-Ux

(2.9)

\-U\ 1

1 - u*x

(2.10)

\-U\

for 0 ^ ux ^ 1. The asymptotic limits (as k tends to infinity) give

c \

-

^ k I max ""

wk

(2.11) C\ + Wi Wi

(2.12)

C\ +W\

with ^ it I max +

*^ifc I max ~

(2.13)

^

Figure 2.11 is a plot of Ck |max against cx for different values of w{. It can be seen that as wx approaches zero, C j m a x reaches unity, resulting in a high correct fused decision rate (similar experimental results achieved with a Bayesian approach are described in chapter 6).

0.8 -f

Wl =

0.5

w^O.2 w,=0.1 0.2

H

1 0.4

1

h 0.6

H

h0.8

1.0

Fig. 2.11 Plot of Ck |max against c{ for different values of wx Two decision fusion cases can be identified: binary decision making and multiple hypothesis decision making. Table 2.2 presents the possible decision outputs for two NDT sensors in the case of binary decision output.

Data fusion system models 19 Table 2.2 Binary decision outputs for multiple NDT sensor outcomes Sensor 1 Sensor 2

Defect

No defect

Uncertain

Defect No defect Uncertain

Defect Uncertain Uncertain

Uncertain No defect Uncertain

Uncertain Uncertain Uncertain

From Shafer56 and Dasarathy57 we can write Ci = i7i%(#/) = 1

(2.48)

i' = 0

Bayesian theory can be adapted for decision making. If multiple sensors are used, the general equation becomes p(tf01 E)p(Hi \E)...p(H,\ E)p(E) P(E/H0

n HX n • • • n H,) =

(2.49) !>(#,)

This theory presents the advantages of giving a belief (probability) on a hypothesis given an evidence (event) (classical inference gives the probability of occurrence of an event given a hypothesis). It uses an a priori probability about the feasibility of a hypothesis. When no a priori information is available, the principle of indifference is used in which the p(Ht) for all / are assumed equal. Some limitations of this theory are: • • • • •

no representation of ignorance is possible; prior probability may be difficult to define; result depends on choice of prior probability; it assumes coherent sources of information; adequate for human assessment (more difficult for machine-driven decision making); • complex with large number of hypotheses; • poor performance with non-informative prior probability (relies on experimental data only). The outcome of Bayesian inference method is a single 'hard' number related to a proposition (single decision probability).

Bayesian estimation is used to eliminate unlikely information/hypotheses and to solve ambiguities and conflicting information from multiple sensors. An example of Bayesian theory applied to NDT is described in chapter 5. 2.3.3 DEMPSTER-SHAFER EVIDENTIAL REASONING The Dempster-Shafer theory is often described as an extension of the probability theory or a generalisation of the Bayesian inference method.2 The Dempster-Shafer theory was

Fusion methodology 27 not specifically designed for reasoning with uncertainty and its application to expert systems and data fusion did not become apparent until the 1980s. Dempster-Shafer theory has been used for assigning a degree of belief in target identification applications10'44 and tactical inferencing.68 Before combining information, the theory of evidence must be presented. It is called a theory of evidence because it deals with weight of evidence. Assume a set of n mutually exclusive and exhaustive propositions, 0 = [X0,XU ..., Xn} where 0 is called a frame of discernment. Thus propositions can be developed by the Boolean operator OR; 2 0 is the set of all the subsets of 0. Dempster-Shafer developed the concept of mass probability to assign evidence to a proposition, which is denoted m(X) where 0 ^ m(Xi) ^ 1

X rn{X) = 1 m(0) = 0 Another term for mass probability is basic probability assignment (bpa). The support for a hypothesis is the total degree of belief for this hypothesis to be true. A belief function can be defined by Bel: 2e -> [0, 1]

Bel(X) = J] ™(r)

for each x

^0

(2 50)

'

YCX

where Bel{X) is the degree of support for the proposition X which for multiple hypotheses becomes Bel(X)= X > ( X , ) xtcx

(2.51)

The properties of a belief function are Bel(G) =1 Bel(X) = 0 0 - i—i—v-r-=\—i

1.0 1-5 Defect depth / mm

1

2.0

i

1 1 1 1

2.5

Fig. 2.19 Sensor models for two NDT systems The objective is to fuse systems 1 and 2 in order to generate a consensus assessment of the defect depth. The Dempster-Shafer rule of combination was applied to integrate information from each system. Each sensor provides the fusion centre with a probability (or belief) of an event. The fusion is performed at a statistical level and not directly at a measurement level (details of the operation are given in chapter 6). The fusion centre collects the information from multiple sensors and produces a global inference operation. The resulting consensus is a third curve (Fig. 2.20), with a greater degree of support than 0.35 - System 1 - System 2 0.25 +

- System 3

* 0

0.2

0.4

0.6

0.8

1.0 1.2 1.4 Defect depth / mm

1.6

I I I i I I 1.8 2.0 2.2 2.4

Fig. 2.20 Fusion of multisensor data using Dempster-Shafer rule of combination

36 Data fusion - a review either of the two models. Using data fusion to combine information from multiple NDT sensors can improve the performance of an inspection. 2.6

Conclusion

The basic principles and most common methodologies of data fusion have been presented in this chapter. The number of publications related to this technology demonstrates its popularity and implementation in a wide range of areas. The potential of data fusion was described and illustrated through several references. As stated by Rothman and Denton43 a successful application of sensor fusion can only be achieved by an understanding of both the data fusion theory and the application domain. Among the data fusion methodologies presented, application of Bayes's theorem and Dempster-Shafer evidential reasoning can be used to model sensor uncertainty. Dempster-Shafer theory provides a powerful methodology for the representation and combination of evidence. More implementation will have to be performed to assess more deeply the potentials and limits of this theory applied to NDT. The wide range of application and the potential of the data fusion process to any scientific application where sensors play a role at any level are demonstrated by the large number of industrial and educational establishments in the world that are involved in data fusion. The automatic fusion of data from multiple sensors and the use of artificial intelligence for decision making have already demonstrated all its capabilities for multiple target tracking, air combat, robotics and air/sea traffic control.59 It has been demonstrated through a practical example, using current instrumentation, that data fusion can improve the accuracy and reliability of non-destructive examinations. Its implementation should not be hindered by ignorance or conservatism. Conventional NDT methods have limited performance; data fusion may be the technology to develop in order to enhance their efficiency. The acceptance of data fusion and artificial intelligence in NDT will require a change in operator training which will be oriented towards new technologies useful for NDT allied to integrity engineering. Problems exist with using data fusion for decision making and it will be some time before machines approach the data fusion capabilities of the human brain. Research needs to be carried out in a range of technical disciplines. Research on neural networks, expert systems and artificial intelligence will certainly produce new developments in various fields which hitherto have benefited very little from these advances. References 1. Van Trees HL. Detection estimation and modulation theory, 1968, Vol. 1, John Wiley and Sons, New York. 2. Dempster AP. A generalization of Bayesian inference, Journal of the Royal Statistical Society, 1968, 30, 205-47. 3. Barnea DI, Silverman HF. A class of algorithm for fast digital image registration, Institute of Electrical and Electronics Engineers Transactions on Computers, Feb. 1972, C-21 (2), 179-86. 4. Goodenough DG, Robson MA. Data fusion and object recognition, Proceedings of Vision Interface Conference, June 1988, Edmonton, Canada, 42pp. 5. Barbera AJ, Fitzgerald ML, Albus JS, Haynes LS. RCS: the NBS real time control system, Robots 8th Conference Proceedings, Detroit, MI, June 1984, 19.1-19.33.

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39

Rothman PL, Denton RV. Fusion or confusion: knowledge or nonsense? Proceedings of the SPIE, 1470, Data Structures and Target Classification, April 1991, Orlando, FL, 2 - 1 2 . Blackman SS. Theoretical approaches to data association and fusion, Proceedings of the SPIE, 931, Sensor Fusion, 4 - 6 April 1988, Orlando, FL, 5 0 - 5 . Schoes J, Castore G. A distributed sensor architecture for advanced aerospace systems, Proceedings of the SPIE, 931, Sensor Fusion, April 1988, Orlando, FL, 74-85. Luo RC, Kay MG. Multisensor integration and fusion: issues and approaches, Proceedings of SPIE, 931, Sensor Fusion, April 1988, Orlando, FL, 4 2 - 9 . Hackett JK, Shah M. Multisensor fusion: a perspective, IEEE CH2876-1/90/00001324, 1990, 1324-30. Pau LF. Sensor data fusion, 1988, Journal of Intelligent and Robotic Systems, 1(2), 103-16. Thomopoulos SCA. Sensor integration and data fusion, Proceedings of the SPIE, 1198, Sensor Fusion II: Human and Machine Strategies, Nov. 1989, Philadelphia, PA, 178-91. Harris CJ. Distributed estimation, inferencing and multi-sensor data fusion for real time supervisory control, Sept. 1989, Proceedings of the Artificial Intelligence in Real-time Control IF AC Workshop, Shenyang, China, 19-24. Tenney RR, Sandell NR Jr. Detection with distributed sensors, July 1981, Institute of Electrical and Electronics Engineers Transactions on Aerospace and Electronic Systems, 17(4), 501-10. Thomopoulos SCA. Theories in distributed decision fusion: comparison and generalization, Proceedings of the SPIE, 1383, Sensor Fusion HI: 3D Perception and Recognition, Nov. 1990, 623-34. Chair Z, Varshney PK. Optimal data fusion in multiple sensors detection system, Jan. 1986 Institute of Electrical and Electronics Engineers Transactions on Aerospace and Electronic Systems, 22(1), 98-101. Viswanathan R, Thomopoulos SCA, Tumuluri R. Optimal serial distributed decision fusion, July 1988, Institute of Electrical and Electronics Engineers Transactions on Aerospace and Electronic Systems, AES-24(4), 366-75. Thomopoulos SCA, Okello NN. Distributed detection with consulting sensors and communication cost, Proceedings of SPIE, 931, Sensor Fusion, April 1988, Orlando, FL, 31-40. Shafer G. A mathematical theory of evidence, 1976, Princeton University Press, Princeton, New Jersey, USA. Dasarathy BV. Decision fusion, 1994, IEEE Computer Society Press. Thomopoulos SCA. Theories in distributed decision fusion, 1991, IF AC Distributed Intelligence Systems, Virginia, USA, 195-200. Abidi MA. Sensor fusion: a new approach and its application, Proceedings of the SPIE, 1198, Sensor Fusion II: Human and Machine Strategies, Nov. 1989, Philadelphia, USA, 235-46. Abdulghafour M, Goddard J, Abidi MA. Non-deterministic approaches in data fusion - A review, Proceedings of the SPIE, 1383, Sensor Fusion HI: 3D Perception and Recognition, Nov. 1990, 596-610. Crowley JL, Demazeau Y. Principles and techniques for sensor data fusion, May 1993, Signal Processing, 32(1-2), 5-27.

40 Data fusion - a review 62. Huntsberger TL, Jayaramamurthy SN. A framework for multi-sensor fusion in the presence of uncertainty, 1987, Proceedings of the 1987 Workshop on Spatial Reasoning andMultisensor Fusion, 5-7 Oct. 1987, AAAI, 345-350. 63. Thomopoulos SCA, Viswanathan R, Bougoulias DC. Optimal Decision Fusion in Multiple Sensor Systems, Sept. 1987, Institute of Electrical and Electronics Engineers Transactions on Aerospace and Electronic Systems, AES-23(5), 644-53. 64. Durrant-Whyte HF. Sensor models and multisensor integration, 1988, 303-12. 65. Durrant-Whyte HF. Consistent integration and propagation of disparate sensor observations, Fall 1987, International Journal of Robotics Research, 6(3), 3-24. 66. Duda RO, Hart E, Nilsson NJ. Subjective Bayesian methods for rule based inference systems, 1976, Proceedings of the National Computer Conference, 1075-82. 67. Malik R, Polkowski E. Morphological technique for combination of sensor readings, Proceedings of the SPIE, 1350, Image Algebra and Morphological Image Processing, 1990,165-76. 68. Dillard RA. Tactical inferencing with the Dempster/Shafer theory of evidence, 1983, Proceedings of the Institute of Electrical and Electronics Engineers 17th Asilomar Conference on Circuits, Systems and Computers. 69. Zadeh LA. Fuzzy logic, April 1988, Institute of Electrical and Electronics Engineers Computer, 94-102. 70. Ferrari C. Coupling fuzzy logic techniques with evidential reasoning for sensor data interpretation, Proceedings of Conference on Intelligent Autonomous Systems 2, Dec. 1989, Amsterdam, Netherlands, 2, 965-71. 71. Russo F, Rampoui G. Fuzzy methods for multisensor data fusion, April 1994, Institute of Electrical and Electronics Engineers Transactions Instrumentation and Measurement, 43(2), 288-94. 72. Mathur B, Wang HT, Liu SC, Koch C, Luo J. Pixel level data fusion: from algorithm to chip, Proceedings of the SPIE, 1473, Visual Information Processing: from Neurons to Chips, April 1991, Orlando, FL, 153-60. 73. Seetharaman G, Chu CHH. Image segmentation by multisensor data fusion, Institute of Electrical and Electronics Engineers Proceedings of 22nd South-eastern Symposium on System Theory, March 1990, Cookeville, USA, 583-7. 74. Duncan JS, Gindi GR, Narendra KS. Low level information fusion: multisensor scene segmentation using learning automata, Proceedings of the 1987 Workshop on Spatial Reasoning and Multisensor Fusion, Oct. 1987, AAAI, 323-33. 75. Tsao TR, Libert JM. Fusion of multiple sensor imagery based on target motion characteristics, Proceedings of the SPIE, 1470, Data Structures and Target Classification, April 1991, Orlando, USA, 37-47. 76. Libby V, Bardin RK. Conversion of sensor data for real time scene generation, Proceedings of the SPIE, 1470, Data Structures and Target Classification, April 1991, Orlando, USA, 59-64. 77. Thomas J. MITAS: multisensor imaging technology for airborne surveillance, Proceedings of the SPIE, 1470, Data Structures and Target Classification, April 1991, Orlando, USA, 65-74. 78. Pau LF. Behavioral knowledge in sensor/data fusion systems, 1990, Journal of Robotic Systems, 7(3), 295-308. 79. Wright WA. A Markov random field approach to data fusion and colour manipulation, May 1989, Image and Vision Computing, 7(2), 144-50.

References 41 80. Xu H. Efficient fusion technique for disparate sensory data, 1991, Institute of Electrical and Electronics Engineers Proceedings of IECOW91, CH2971-9/91/ 0000-2535, 2535-40. 81. Beard W, Jones A. Harnessing neural network, Dec. 1990, Electronics World and Wireless World, 1047-52. 82. Learning with neural network, August 1993, Laboratory Equipment Digest, 8-9. 83. Charlton PC. Investigation into the suitability of a neural network classifier for use in an automated tube inspection system, August 1993, British Journal of Non Destructive Testing, 35(8), 433-7. 84. Whittington G, Spraclen T. The application of a neural network model to sensor data fusion, Proceedings of the SPIE, 1294, Applications of Artificial Neural Networks, April 1990, Orlando, USA, 276-83. 85. Eggers M, Khuon T. Neural network data fusion concepts and application, Institute of Electrical and Electronics Engineers Proceedings of International Joint Conference on Neural Networks, June 1990, San Diego, USA, II.7-II.16. 86. Ruck DW, Rogers SK, Kabrisky M, Mills JP. Multisensor fusion classification with a multilayer Perceptron, Institute of Electrical and Electronics Engineers Proceedings of International Joint Conference on Neural Networks, June 1990, San Diego, USA, H.863-H.868. 87. Rajapakse J, Acharya R. Multisensor data fusion within hierarchical neural networks, Institute of Electrical and Electronics Engineers Proceedings of International Joint Conference on Neural Networks, June 1990, San Diego, USA, II.17-II.22. 88. Chilips ML, Steele N.F. Non destructive evaluation using neural network, Nov./ Dec. 1989, Nuclear Plant Journal, 44-50. 89. Kjell BP, Wang PY. Data fusion and image segmentation using hierarchical simulated annealing on the connection machine, Proceedings of the SPIE, 1002, Intelligent Robots and Computer Vision, Nov. 1988, Cambridge, USA, 330-7. 90. Chen S. Adaptive control of multisensor systems, Proceedings of the SPIE, 931, Sensor Fusion, April 1988, Orlando, USA, 98-102. 91. Udpa L, Udpa S. Application of neural network to non destructive evaluation, Oct. 1989, Report, Colorado State University, 143-7. 92. Udpa L, Udpa SS. Eddy current defect characterization using neural network, March 1990, Materials Evaluation, 48, 342-53. 93. Windson CG, Anselme F, Capineri L, Mason JP. The classification of weld defects from ultrasonic images: a neural network approach, Jan. 1993, British Journal of Non Destructive Testing, 35(1), 15-22. 94. Blackman SS, Broida TJ. Multiple sensor data association and fusion in aerospace applications, June 1990, Journal of Robotic Systems, 7(3), 445-85. 95. Easthope PF, Goodchild EJG, Rhodes SL. A computationally tractable approach to real time multi-sensor data fusion, Proceedings of the SPIE, 1096, Signal and Data Processing of Small Targets, March 1989, Orlando, USA, 298-308. 96. Deb S, Mallubhatla R, Pattipati K, Bar-Shalom Y. A multisensor multitarget data association algorithm for heterogeneous sensors, 1992, Proceedings of the 1992 American Control Conference, IEEE 92CH3072-6, 2, 1779-83. 97. Thompson WE, Parra-Loera R, Ta CWO. A Pseudo k-means approach to the multisensor multitarget tracking problem, Proceedings of the SPIE, 1470, Data Structures and Target Classification, April 1991, Orlando, USA, 48-58.

42 Data fusion - a review 98. Gerhart G, Martin G, Gonda T. Thermal Image Modeling, Proceedings of the SPIE, 782, Infrared Sensors and Sensor Fusion, May 1987, Orlando, Florida, USA, 3-9. 99. Macnicol G. Complex Imagery, Sept. 1991, Computer Graphics World, 75-9. 100. Henderson T, Weitz E, Hansen C, Mitiche A. Multisensor knowledge systems: Interpreting 3-D structure, Dec. 1988, International Journal of Robotics Research, 7(6), 114-37. 101. Lee RH, Leahy R. Segmentation of multisensor images, Proceedings of the 6th Multi-dimensional Signal Processing Workshop, Sept. 1989, Pacific Grove, USA, 23. 102. Popovic D, Heine R, Scharne T, Wolter F. Search strategies for collision-free path planning for robot manipulators, Robotersysteme, 8(2), 1992, 67-73. 103. Gros XE, Strachan P, Lowden DW, Edwards I. NDT Data Fusion, 1994, 6th European Conference on Non Destructive Testing, Nice, France, Oct. 1994.

3

Non-destructive Testing Techniques Non-destructive testing has no clearly defined boundaries R. Halmshaw, 1991

3.1

Introduction

The structural integrity of materials, components and structures has to be assessed for quality control, safety regulations and product specifications. Numerous testing techniques have been developed for maintenance and condition monitoring. These techniques can be categorised into two main classes: destructive testing, based on fracture mechanics, and non-destructive testing which leaves the inspected component undamaged. Non-destructive testing (NDT) is particularly relevant to the inspection of large and expensive components. The aerospace, food, nuclear and offshore industries are only a few examples of industries which employ a wide range of NDT techniques. The most commonly used NDT methods in industry include visual inspection, liquid penetrant inspection, magnetic particle inspection, eddy current testing, alternating current potential drop, alternating current field measurement, ultrasonic testing and radiography. These NDT techniques can be used for: • • • • •

the detection of unwanted discontinuities and separations in a material (flaws); structural assessment of a component (microstructure and matrix structure); metrology and dimensional purposes (thickness measurement, checking of displacement and alignment); determination of physical properties of a material (electrical, magnetic or mechanical properties); the detection of foreign bodies in food.

The principal objective of a non-destructive examination (NDE) is to provide the inspector with quantitative as well as qualitative information. This is achieved by detecting, locating and sizing any detected flaws. Several types of defect exist, for example cracks, voids, corrosion, inclusions, delamination, impact damage and holes. These defects begin as minor flaws which can occur as the result of excessive loading or external stresses applied to a material. If not discovered at an early stage, they may develop into dangerous faults. Defect quantification requires considerable skill and experience, very often leading to the use of more than one NDT method, owing to the fact that each method is able to provide limited information on a particular category of defect. For example, eddy

44 Non-destructive testing techniques current testing will allow detection of surface defects but internal defects will remain unseen by this method. Therefore, the use of another method such as ultrasound will be required. The terms 'technique' and 'method' have been cautiously applied as follows: method will be used for the description of a discipline, such as ultrasonic inspection; and pulseecho or through-transmission will be qualified as techniques. NDT methods make use of physical principles such as electromagnetism and optics, and an understanding of the physics of the inspection methods is required to ensure an effective inspection procedure. The limitations and advantages of the most common NDT methods suited to data fusion are reviewed in this chapter. The sensitivity and accuracy of each method is dependent upon its application, and the performance of NDT systems can be compared and assessed using probabilistic and statistical analysis. Such studies are becoming more common in industry in order to define the best suited method or defect detection technique for a specific application. These performance criteria will be discussed in this chapter. Less common methods such as infrared thermography and acoustic emission, and more specialised methods including proton annihilation, neutron scattering and microwaves, are briefly described below. This list is not exhaustive as new methods are continuously being developed. Not all possible NDT methods are presented in this chapter, and additional information on NDT can be found in the literature.1"5 3.2

Visual inspection

Visual inspection is the original method of NDT and should not be neglected when performing a NDE. Direct visual inspection, with the naked eye or with optical aids such as a magnifying glass, microscope, lamp, camera or horoscope, is still the first step to be carried out in a NDE and is usually followed by more sophisticated NDT methods.6 3.2.1 MICROSCOPY Microscopy and macro-photography can be used for non-destructive examination of components. Traditionally a light microscope is used to observe and characterise the surface structure up to the limit of magnification of the apparatus. Spatial resolution, depth of focus and magnification are limiting factors. Figure 3.1 is a micrograph of the surface of a composite material showing damages to composite fibres and matrix breakage. Image processing developments have greatly improved the quality of visual inspection. Photographs or images from boroscopes, microscopes and video cameras can now be enhanced using smoothing and filtering facilities. 3.2.2 BOROSCQPY Boroscopes, also known as endoscopes and introscopes, can be used for the inspection of areas which otherwise are not accessible without disassembly.7 A horoscope is an instrument used for inspecting cavities and is normally composed of an illuminating light and a miniature camera. The use of optical fibres makes boroscopic equipment flexible, allowing the inspection of hollow components, such as tubes or pipes, and cavities where there is no straight or direct access. Boroscopes 6 m long with a direction of view from 0 ° to 100° and magnification facilities are now available. They can also be coupled to a closed-circuit television camera (CCTV), providing the operator with an image on a

Visual inspection

45

Fig. 3.1 Micrograph of the surface of a composite material showing broken fibres and matrix breakage after impact damage (magnification: x 100) screen which can be recorded on a videotape. A remote viewing facility finds applications in the inspection of valves, pistons and cylinders of an engine, drugs or explosives searching by security officers, inspection of combustion chambers in aircraft, corrosion mapping in pressure tanks and nuclear reactor vessel inspection. 3.2.3 LASER HOLOGRAPHY Laser holography can be used in NDE to measure deformation of components under stress, to detect the presence of a defect or to measure the surface uniformity of a component. The general principle of holography is as follows: a 3-D image is obtained by recording the interference of a diffusely reflected wavefront by an object with a reference wavefront (Fig. 3.2). Laser holography makes use of electromagnetic waves from visible, coherent laser light (typically a H e - N e gas laser). Stable conditions are required during the Mirror

Object

Mirror

Holographic Film Fig. 3.2 Recording of a laser hologram

46 Non-destructive testing techniques exposure time. Pulse lasers reduce the exposure time and can be used where stable conditions cannot be achieved. Image reconstruction can be performed by lighting the hologram with the same reference beam. An observer will be able to view a virtual 3-D image of the object through the hologram (Fig. 3.3). Mirror BeamSplitter

LASER

^

^^

\ \

y^

s'

V\

W

U

J

./Reference

J^

Beam

•YK; Mirror \\

\

gg \

yfOS

/rOS

\

\

A

/

\

JVirtual Image

/

= t / ^ Hologram jrv, ^^Observer Fig. 3.3 Reconstruction of a laser hologram

Components inspected by laser holography are usually heated slightly and placed under stress or subjected to small vibrations during inspection, and a differential test is performed by comparing interference fringes of the specimen under stress with reference fringes of the static component. Laser holography is used for the inspection of pneumatic tyres, turbine blades and honeycomb composite structures in the aerospace industry.8 However, it is limited to surface examination and provides only qualitative defect information. In summary, visual examination techniques can be described as limited to the detection of surface-breaking defects, location of corrosion and surface roughness assessments. Their main advantage is that results are displayed in a visual format which can be readily interpreted by an inspector. With visual equipment being continuously updated and coupled with image processing equipment, visual inspection still has an important place in NDT and should not be neglected. 3.3

Liquid penetrant inspection

Liquid penetrant inspection is a low cost method, easy to apply and used to detect surface breaking defects such as cracks, laps and porosity in forging, castings, ceramics and nonporous materials.9 This method can be described as an extension of visual inspection but with a greater sensitivity. Large areas can be inspected but liquid penetrant is a slow process in terms of application and flaw indication. The principle of liquid penetrant inspection consists of spraying a coloured dye onto the surface of a component which seeps into any surface opening by capillary action. The liquid concentrates into cavities and, after removal of excess penetrant, is made visible by applying a developer, which reverses the capillary action, to the surface of the specimen. As a manual process, liquid penetrant can be very time consuming for large scale inspection tasks. It can be automated, using robotics and pattern recognition facilities, for on-line manufacture examination.

Liquid penetrant inspection 47 Liquid penetrant inspection is a six-stage process (Fig. 3.4) which is performed as follows: • • • • • •

The surface of the component inspected should be cleaned and dried prior to inspection. A coloured liquid penetrant is sprayed onto the surface of the component. The excess liquid is removed from the surface by rinsing with water or a chemical. A developer is applied over the surface of the component to reveal liquid penetrant trapped in defects by chemical reaction. Inspection of the component is performed and defects located. Post-cleaning of the specimen is carried out after inspection. Crack

. Surface cleaning + drying

4. Application of developer

2. Application of liquid penetrant + dwell

3. Removal of excess penetrant + rinse + dry

5. Inspection

6. Surface post-cleaning

Fig. 3.4 Liquid penetrant inspection procedure

Three types of liquid penetrant can be used: water soluble, post-emulsifiable and solvent removable. Water soluble penetrant can be washed directly with water and is the most widely used type of penetrant. Post-emulsifiable dyes are oil based penetrants which require the use of an emulsifier prior to water washing. They are more costly than water washable penetrants but have a greater defect sensitivity. Solvent removable penetrants are used for on-site inspection of large workpieces. They are oil based penetrants which require a chemical solvent for cleaning. Liquid penetrants can be visible under white light and fluorescent ultraviolet (UV) light. The choice of penetrant depends upon the sensitivity required, the size of the specimen inspected, accessibility and the cost of the inspection. The different sensitivities and relative costs of dye penetrants are shown in Fig. 3.5. Surface cleanliness and roughness, and the size, shape and accessibility of the sample are limiting factors of liquid penetrant inspection. This method is usually used for testing non-ferrous components such as austenitic stainless steel, and is widely used in the aircraft industry to inspect ceramics and structural weldments. Magnetic particle inspection is considered more appropriate on ferrous or magnetisable materials.

48

Non-destructive testing techniques Post-emulsifiable fluorescent

High Sensitivity + Cost

Solvent removable fluorescent Water washable fluorescent Post-emulsifiable visible dye Solvent removable visible dye Water washable visible dye

Low Sensitivity + Cost

Fig. 3.5 Difference in sensitivity and cost for various liquid penetrants

3.4

Magnetic particle inspection

Magnetic particle inspection (MPI) is used for the detection of surface breaking cracks in ferromagnetic materials. 10-12 It is one of the most extensively used electromagnetic methods in industry as it is easy to apply and provides a direct visual indication of surface breaking cracks. Magnetic particle inspection involves magnetisation by the application of a permanent magnet, electromagnet or electric current to the surface of the component inspected. This produces a magnetic field inside the material which becomes distorted by the presence of a flaw, causing a local magnetic flux leakage (Fig. 3.6).

Surface Breaking Crack

Magnetic Particles

Ferromagnetic Material

Magneti Flux Lines Fig. 3.6 Magnetic leakage flux in the vicinity of a surface breaking crack and accumulation of magnetic particles Fine ferromagnetic particles in the form of dry powder or suspended in a liquid (oil or water) are then sprayed onto the surface of the specimen to reveal the leakage field. Electromagnetic particles are available as daylight visible or UV fluorescent particles (Fig. 3.7(a) and (b)). Their size ranges from 1 to 25 pm for wet particles and up to 150 pm for dry particles. The particles are attracted by the leakage field and accumulate in the vicinity of the crack which is subsequently made visible (Fig. 3.6). The greatest leakage flux for a given test field is obtained for flaws positioned at right angles to the lines of force. For detection, flaws should lie between 45° and 90° to the magnetic field, which is commonly applied in two directions at right angles.

Magnetic particle inspection 49

Fig. 3.7 (a) Daylight and (b)fluorescentmagnetic particle detection of a HAZ crack in a butt welded plate of medium carbon steel

3.4.1 MAGNETIC HYSTERESIS The magnetic flux density, B, induced in the test zone is a function of the magnetic permeability of the material, jun and the applied magnetic field, H: where B is measured in tesla (T), H in ampere per metre (A/m) and /ur and JU0 in henry per metre (H/m). /u0 is the permeability of free space: JU0 = 4TZ 10 "7 H/m. The value of JUTvaries with the material composition; /uT=l for non-ferromagnetic material. The magnetisation curve B = / ( / / ) varies in a non-linear manner (Fig. 3.8). For unmagnetised specimens, the value of B is null (point a) and increases up to a saturation value Bs. If a ferromagnetic material is magnetised to saturation its relative permeability is equal to unity. After saturation of a component, any increase in the magnetic field strength H

Fig. 3.8 Typical hysteresis curve for a ferromagnetic material

50 Non-destructive testing techniques due to the presence of a defect leads to a decrease in permeability and flux leakage occurs. If the magnetic field H is reduced to zero, the value of B does not return to zero but to a value Br called the remanent flux density. By reversing the direction of H, B returns to zero (point b) where the distance ab is known as the coercive force of the material. If H is increased further, B decreases up to a negative saturation value (-Bs). Finally, if H is reduced to zero, the plot of B against H does not retrace its original path but follows the path cde and eventually reaches Bs again. The plot of B against H is known as a hysteresis curve and is shown in Fig. 3.8. The behaviour of electromagnetic waves in conducting materials can be derived from Maxwell's equations. It is not in the scope of this book to investigate further these equations which can be found in Reference 11. Several procedures of magnetisation are summarised in Table 3.1, and include prods, permanent magnets, electromagnetic coils, yokes and flexible cables. Prods are current flow techniques and permanent magnets, electromagnetic coils, yokes and flexible cables are magnetic flow techniques. Permanent magnets and yokes are well suited for on-site and laboratory inspection but are limited to the coverage of small areas. Flexible cables and prods are more useful for the magnetisation of large specimens. The best inspection results are usually achieved on materials with high relative permeability.

Table 3.1 Advantages and limitations of magnetising techniques used for MPI Magnetising technique Advantages Permanent magnet

Electromagnetic coil

Yoke

Flexible cable

Prod contacts

Portable Low cost Limited test area Magnetises the specimen parallel to the axis of the coil Very uniform field

Portable Adjustable shape and design Easy to manipulate Magnetisation of large specimens No danger of burn marks Efficient for underwater inspection of pipes Hand held Useful for magnetisation of large areas

Restrictions Field magnitude limited Magnet difficult to remove from component Mainly for bar-shaped materials Magnetisation uniformity is affected by the position of the specimen within the coil Positioning is important Good contact with specimen is required Field limited to the outer surface of the pipe Voltage requirements increase with cable length Difficult to position Can cause current arc damage (burn marks) Requires high current supply

Eddy current testing 51 3.4.2 MAGNETISING CURRENT The choice of the magnetising current, AC or DC, depends upon the material to be inspected and the type of defect sought. Alternating current is preferred for the inspection of soft materials, such as pure iron and low carbon steels, as it produces excellent mobility of the particles and demagnetisation is usually not required (the higher the magnetic permeability, the easier the magnetisation). Moreover, AC confines the magnetic field to the surface of the specimen which makes the technique very efficient for the detection of surface breaking defects. Hard materials (alloy steels, high carbon steel) are difficult to magnetise and exhibit a high remanence. The field created with DC penetrates deeper through the component inspected and sub-surface discontinuities can sometimes be detected. Direct current magnetising techniques introduce a constant magnetic flux in the material. Demagnetisation of the specimen is required after inspection, especially with hard materials, as: • • •

residual fields may interfere with magnetically sensitive components; abrasive particles may be attracted to magnetised areas; with electric arc welding, the arc may be deflected.

Demagnetisation can be performed by submitting the component to a continuously reversing magnetic field of decreasing strength. Magnetic particle inspection is a well established method and favoured by industry as it is low cost and portable and provides the operator with an immediate visual display of the flaw. However, quantitative information other than the length of the defect cannot be obtained except by using filing or grind methods. It is limited to the detection of surface and surface-breaking flaws in ferromagnetic materials, and the sensitivity of the method is dependent upon multiple parameters such as the magnetisation technique and the electromagnetic properties of the material inspected, as well as the size, shape and orientation of the defect. Automatic MPI, using video cameras and image processing facilities, has been developed for the examination of blade roots, rotor grooves of turbine rotors and butt welds of pressure vessels.13 3.5

Eddy current testing

The eddy current method uses the principle of electromagnetic induction to inspect a component.14-19 A magnetic field which varies with time induces electrical currents in conducting materials. The currents are called Courants de Foucault, from the physicist of the same name who discovered their presence, but are more commonly known as eddy currents. The presence of a flaw affects the formation of eddy currents, and this perturbation can be measured to locate and quantify defects. The eddy current method can be applied to the inspection of any electrically conductive material for detection of surface and sub-surface defects as well as corrosion mapping. Eddy currents can also be used for monitoring crack growth, as this is a very reproducible method of inspection. 3.5.1 CONVENTIONAL EDDY CURRENT TESTING An alternating current of fixed frequency sent to a coil creates a magnetic field in the vicinity of the coil. The alternating magnetic field is perpendicular to the direction of the

52 Non-destructive testing techniques current and parallel to the axis of the coil. The coil is held in a probe which is scanned over the surface of a component. If the coil is brought into proximity with a conductive material, the magnetic field in the coil induces electrical 'eddy currents' in the material. These currents give rise to a secondary magnetic field in the specimen called the 'induced magnetic field'. According to Lenz's law, the induced magnetic field is of equal magnitude but has a polarity which opposes the original magnetic flux in a non-ferromagnetic metal. The presence of a discontinuity on the surface of the component inspected will perturb the induced magnetic field and will also affect the eddy currents. The eddy current variation is recorded by measuring the changes in electrical impedance of the coil in terms of magnitude and phase. The output of the coil is usually displayed on the cathode ray tube (CRT) of an oscilloscope. An eddy current transducer can be electrically represented as a resistance and a coil in series (Fig. 3.9).

I

©


20 kHz). The waves are propagated into an elastic medium and are detected either by the same or by a different transducer. An emitter probe containing a piezoelectric crystal generates high frequency ultrasounds (0.1-25.0 MHz) which are injected into a material by placing the probe in contact with the surface of the component inspected. The sound wave propagates through the specimen inspected and is reflected from the far surface known as the backwall echo. The reflected beam is detected by a receiver probe, or the same probe in the case of pulse-receive systems, and the signal is displayed on a CRT as an A-scan plot of signal amplitude versus time (Fig. 3.16). The

Ultrasonic Transducer Test Sample

Flaw

t -

Backwall I Echo Flaw Echo J-J±do

time

Fig. 3.16 Typical ultrasonic pulse echo system and A-scan plot

60 Non-destructive testing techniques transmitted pulse is indicated by the first rise on the screen of the CRT; the second rise is the reflected pulse either from the backwall of the specimen or from a defect. By measuring the time taken for the two pulses to travel through the specimen, the thickness of a component or the position of a defect can be accurately measured. If the speed of the ultrasonic wave in the material inspected is v (m/s) and if tx (s) is the time measured between the two peaks, the distance d0 (m) of the defect from the surface of the specimen can be calculated from vtx ^o=-i 2

(3.5)

and the ultrasonic wavelength X (m) is given by v X=/

(3.6)

where / is the frequency in hertz (Hz) of the ultrasonic wave. Two techniques, known as 6 dB drop and 20 dB drop, are commonly used for sizing of defects. The decrease in signal amplitude caused by aflawis used as an indicator of flaw dimension. The ultrasonic testing technique using the transit time of an acoustic wave to measure the distance of a flaw from the probe and the amplitude of the reflected wave to size this flaw is called the pulse echo technique. In this technique, a single probe is used to transmit and receive the ultrasonic signal. Another technique called throughtransmission uses two transducers (a transmitter and a receiver). This technique requires access to both sides of the specimen, as the receiver is generally placed on the opposite side. Ultrasonic signals can be displayed as A-scan, B-scan or C-scan formats. A typical Ascan plot is shown in Fig. 3.16. Both B-scan and C-scan formats display a 2-D ultrasonic image of a defect. In the case of a B-scan, the displayed signal is time versus a linear position. With a C-scan a 2-D image of the signal amplitude at a particular depth range and over a surface is generated. C-scan systems are usually computer controlled and the image displayed is colour coded to facilitate interpretation. Ultrasonic systems displaying both Bscan and C-scan formats are called P-scan systems. 3.8.2

ULTRASONIC TRANSDUCERS

There are four main types of ultrasonic transducer:33'34 normal, single crystal, twin and angle beam (Table 3.3). Designs of single crystal probe and angle beam probe are shown in Figs 3.17 and 3.18. A variable angle probe (VAP), developed by Babcock, can be sequenced through up to eight different shear wave angles using a curved piezoelectric crystal mounted on the circumference of a hemi-cylindrical perspex shoe.35 Beyond the plastic window of a probe, three regions characterise the behaviour of an ultrasonic wave: the dead zone, the near field zone and the far zone (Fig. 3.19). The dead zone - due to the transmission pulse width - is a region immediately beneath the entry surface from which no reflection from flaws can be observed. The near zone, or Fresnel region, is the region in an ultrasonic beam which is subject to complex interference due to diffraction effects. Sizing of flaws should be avoided in this region. The near field zone can

Table 3.3 Different types of ultrasonic transducers Ultrasonic transducer

Characteristic

Normal probe

Generates longitudinal waves Separate transmitter and receiver A single crystal both transmits and receives the ultrasonic signal Transmitter and receiver in same housing but electrically and acoustically separated Produces an ultrasonic beam which is introduced at an angle into the material Typical angles: 30°, 45°, 60°, 70°, 80°

Single crystal probe Twin crystal probe

Angle beam probe

Coaxial Cable

Metal Case Backing Material Rubber Insulator Electrode Piezoelectric Crystal Electrode Plastic Window Fig. 3.17 Design of an ultrasonic compressional wave transducer

Electrodes Backing Material Piezoelectric Crystal Perspex Wedge Fig. 3.18 Design of a shear wave ultrasonic transducer

62

Non-destructive testing techniques

Boundary of the Beam

D

Dead Zone

Far Field Zone

Near Field Zone

Fig. 3.19 Schematic representation of the ultrasonic beam intensity

be calculated from 2

-D

-X..2

N= AX

D*

« — AX

2

2

(forD 2 » A2)

(3.7)

where D is the diameter of the probe. The far zone, or Fraunhofer region, is the region in an ultrasonic beam where the energy of the beam decays exponentially. In this region, the intensity of the ultrasonic wave is inversely proportional to the square of the distance from the transmitter. Flaw sizing is performed in this region using the 6 dB or 20 dB drop technique. Krautkramer and Krautkramer32 demonstrated how distance-gain-size (DGS) diagrams can be used to estimate the size of a defect. A DGS diagram is a plot of signal amplitude (dB) against the near field length for different values of the gain (G) of the ultrasonic instrument. The minimum detectable size of a defect for a particular ultrasonic sensor can be estimated as follows: G=

defect size probe diameter

(3.8)

3.8.3 ULTRASONIC WAVE PROPAGATION Because air is a poor transmitter of sound, a couplant is generally used between the ultrasonic probe and the surface of the specimen inspected in order to increase the amount of ultrasonic energy transmitted.36 The couplant can be a gel or water. A common technique involves immersion of the specimen in a water or oil tank and the use of an automated scanning system for inspection. Reflection of ultrasonic waves between two materials depends on their acoustic impedance (Fig. 3.20) which determines the amount of reflection of the ultrasonic wave

Ultrasonic testing 63

Medium \,Z\,V\ Medium 2, Z2, v2

Fig. 3.20 Compressional ultrasonic incident, reflected and transmitted waves and is given by (3.9) where Z, is the acoustic impedance of a longitudinal wave (kg/m2 s), p is the material density (kg/m3) and u, is the velocity of the longitudinal wave in the material (m/s). Longitudinal, or compressional, waves travel in the direction of molecules in a material at the velocity v: (3.10) where E is the elastic modulus of the material (N/m2). When the angle of incidence is zero (Fig. 3.21), the reflection coefficient R and the transmission factor T for compressional and transverse waves are given by /.

IZ2-ZX

A

Ui+Z2

T=l-R

=- =

(3.11)

/,

4Z,Z 2

/i

iz,+z2y

(3.12)

The reflection and transmission coefficients of an ultrasonic wave if medium 1 is steel and medium 2 is copper, for example, are respectively 0.001 and 0.999. This shows that almost the whole of the incident wave is transmitted. In the case of a solid/liquid interface, the transverse wave is always completely reflected. For example, with a steel/water interface, R = 0.871 and T = 0.125. The behaviour of an ultrasonic wave of velocity vx arriving at an angle a on an interface between two materials can be described by a physical law called Snell's law (Fig. 3.21): sin a

sin /?

sin 0

sin d

vs\

vC\

^S2

1^C2

(3.13)

64 Non-destructive testing techniques

S2^

(v!>v2)

Fig. 3.21 Shear waves on an interface between two media

where a is the angle of incidence and reflection, and /? the angle of refraction. Angle waves are often called shear waves. Attenuation of ultrasonic waves varies with the type of material. Material inhomogeneities such as crystal discontinuities, mixed microstructure, anisotropic material, large grain size and low acoustic impedance cause beam scattering and interference effects as a result of diffraction. Austenitic stainless steel, nickel-chromium alloys (Inconel, Incoloy) and copper castings have large anisotropic grains (Fig. 3.22) and are difficult to inspect as they produce severe attenuation and scattering.37

4.80KX 18UH-

10KU HD*8HN

S^8Sil3

P*808*1

.yy$j«

is •". J r

1

* i-

«lr

•#

•&•'"

(3.15)

where / is the intensity of the emergent radiation, I0 is the initial intensity, t is the material thickness (m) and // is the linear absorption coefficient of the material (m _1 ). The value of t required to reduce / by 50 per cent is called the half-value thickness and is given by r=

0.693

(3.16)

Equation (3.15) can be rewritten as / = / 0 2"' /r = / 0 antilogj ^ ^

•Pr*)I

(3.17)

The sensitivity of a radiograph can be estimated using an image quality indicator (IQI). The IQI sensitivity test accepted by the UK, the USA and Germany is the wire IQI sensitivity: .. . diameter of smallest discernible wire wire IQI sensitivity = thickness of specimen Geometry of image formation The image formation is dependent on the amount of energy absorbed by a material and on the characteristics of the source of radiation used as well as the sensitivity of the radiographic film. Lead screens are sometimes used to improve the image quality of the radiographs. Their effect is to absorb scattered radiation.48 The sharpness of a radiograph will depend on several factors such as the source of radiation, the size of the source (5 in Fig. 3.24), the source to film distance (L in Fig. 3.24), the density of the film, the exposure time and the source-specimen orientation. Ug is the geometric unsharpness as described in Fig. 3.24 and can be expressed as ST Ug =

(3.18) L-T It can be minimised by using a smaller focal spot size or by increasing the distance between the source and the detector. 3.9.2 PROGRESS IN RADIOGRAPHIC TECHNIQUES The development of techniques such as real-time radiography (RTR) and computer tomography (CT) has enhanced the quality and efficiency of radiography by providing instantaneous radiographs in 2-D and 3-D formats.

Radiographic inspection 69 Real-time radiography (RTR), also known as radioscopy and fluoroscopy, was first, and still is, used by customs officers for baggage inspection.49 Real-time radiography produces instantaneous radiographs which are displayed on a TV monitor. With RTR, there is no need for film processing, as the film is replaced by a fluorescent screen, an image intensifier tube (IIT) or a scanning linear array system sensitive to radiation. Figure 3.26 is a schematic diagram of an RTR system using an IIT. Real-time radiography is a technique that lends itself to automation for on-line inspection at the manufacturing level and is well suited for remote operation. Automatic welding inspection systems using pattern recognition and image processing have been developed and have proved to be very efficient.50"52 Research that has been carried out in the use of RTR for underwater inspection has demonstrated its full potential.53 Notwithstanding its high capital investment, modern computer and image processing facilities have rendered RTR potentially more cost-effective than film for large operations.

pt X-ray Source

CCTV Test Piece ADC Internal Flaw Image Processing

TV Monitor

Recorder

Fig. 3.26 Schematic diagram of an RTR system using an image intensifier tube

Computer tomography (CT) produces highly detailed images of the inside of a specimen.54 It allows the generation of 3-D images of a defect and component inspected by creating an image of a cross-section of an object (Fig. 3.27). The principle of CT involves passing an X-ray beam through a test piece and recording the emergent photons at multiple angles (Fig. 3.28). This operation is performed by moving the source around the object or rotating the object itself. A computerised reconstruction algorithm maps the points in a cross-sectional area. Image processing is usually required as artefacts are often produced in the image when the photons are absorbed differentially through the object. CT is greatly used for turbine blade inspection in the aerospace and nuclear industries.55 Neutron radiography uses low energy neutrons (cold neutrons, 0.0025 eV) to produce an image.56 The absorption of neutrons in materials such as aluminium, iron and lead is much less than that of X-rays. This technique finds application in the inspection of components of large thickness, explosive fillings and detection of corrosion. The

70 Non-destructive testing techniques HC3 FRONT

£3

1 L E F T

1

CM

Fig. 3.27 Computer tomography image of a section of a helicopter rotor blade

Array of Detectors

Object

^

p^-Rotating Table X-ray Source

r

Computer

Signal Processing

2D/3D Display

Image Reconstruction

Fig. 3.28 Schematic layout of a CT inspection system

production of low energy neutrons from atomic reactors and particle accelerators is time consuming and costly, limiting its application and portability. X-ray fluorescence is a metrologic technique which can be applied to thickness measurement and detection of corrosion. The specimen inspected is irradiated by X-rays and its surface produces fluorescence which is detected by a scintillator. The result is indicated on a coordinate plotter for quantitative analysis. Metallurgical specimens of small size and area can be inspected with this technique. 3.9.3 TYPES OF INSPECTION Forensic science and medicine remain the main areas of application of radiography. Radiography is also used for the inspection of welds, pipes and pressure vessels. The

Additional NDT methods 71 procedure for weld inspection can be found in British Standards BS 2600 and BS 2910. Inspection of rocket parts, detection of foreign objects in food, assessment of the structural integrity of buildings and examination of the construction of statues and artistic objects are currently performed with radiography.57 This method allows the detection of internal flaws and a measure of specimen thickness. Radiography requires large investment and the equipment is not fully portable. Moreover, it is still a hazardous method, and strict control and safety regulations need to be followed to ensure the safety of the inspector. Flaws have to be large enough and positioned in a direction parallel to the radiation beam to be detected, and access to two sides of the component to be inspected is necessary. In spite of these disadvantages, radiography is widely used in industry as it can be automated and provides visual information of the internal parts of an object. Inspection can be fully automated using realtime radiography equipment and pattern recognition facilities.58 3.10

Additional NDT methods

Non-destructive testing methods are not limited to those previously described; in this section, less common and more specialised methods and techniques are briefly presented. 3.10.1 ACOUSTIC EMISSION Plastic deformation and crack growth can result in the generation of acoustic signals. Acoustic emission inspection is principally used for crack monitoring and defect location.59,60 Acoustic emissions are high frequency stress waves generated by the release of strain energy from a material under stress. Because static flaws cannot be detected, external stress is usually applied to the material. This release of energy can be recorded and analysed. Sensitive instrumentation for filtering and signal processing is required in order to obtain optimum results. This method is well suited for laboratory experiments but can be very time consuming and difficult for on-site monitoring. 3.10.2 SQUID MAGNETOMETERS Superconducting quantum interference devices (SQUID) have been developed for precise and accurate measurement of small variations in electromagnetic fields.61 A SQUID consists of a superconducting coil which generates a very stable field. This acts as a polarising field which is distorted by variations in permeability in ferromagnetic materials. These distortions can be detected by a SQUID. Magnetometers are limited to laboratory applications as they operate at the temperature of liquid helium and therefore require cooling equipment. 3.10.3 INFRARED THERMOGRAPHY Infrared thermography is the mapping of isotherms over the surface of a component using heat-sensitive devices.62 An infrared camera scans the surface of a component and records any changes in temperature. The presence of a defect appears as a cold or hot spot and can be rapidly located. Very often, the component inspected has to be pre-heated before inspection. Thermography is a non-contact method which provides rapid visual qualitative information about the structural integrity of a material. It is used for condition monitoring of composite materials and concrete and to check alignment of parts.

72 Non-destructive testing techniques 3.10.4 MICROWAVE INSPECTION Microwaves are waves of electromagnetic radiation (0.001 ^ X ^ 0.1 m) which are directed towards an object and propagate through the material.63 A phase detector compares the refracted wave with a reference signal. A flaw will act as a reflector which will perturb the refracted wave. Because microwaves do not penetrate deeply into metals, this method is restricted to thickness measurement of thin metallic coatings, determination of voids and inclusions in ceramics, plastics and insulators, and the control of homogeneity. 3.10.5 SHEAROGRAPHY Shearography has been developed for the inspection of composite materials and honeycomb structures.64 The technique uses a laser based interferometer which produces two overlapping sheared images of a component under stress. These images interfere at paired points and are detected by a CCD camera. Surface strains due to sub-surface flaws on the component inspected are made visible by analysing the fringe pattern induced. Strain anomalies on the interferogram are characteristic of the presence of a flaw. 3.10.6 LEAK DETECTION For the detection of leakage, a search gas is injected into a sealed enclosure and leakage is detected using a vacuum or pressure gauge (also called a sniffer). Leak detection can be performed on non-porous materials for quality control of seals in glass envelopes, vacuum chambers and containers. Porosity, holes, cracks and lack of seals can be detected, although their location can be difficult to define. 3.10.7 ACOUSTIC IMPACT Acoustic impact is also known as coin tapping and consists of mechanically tapping a material onto the surface of a component. This causes mechanical acoustic vibrations which are detected by a sonometer or by ear. Any change in sound is indicative of anomalies or flaws. This very low cost technique is mainly a manual and slow process, and sensitivity is limited to the hearing of the inspector. Its main application is for the detection of cracks, disbonds and delaminations in metals and composites (honeycomb structures and helicopter rotor blades, for example). 3.11

Computers in NDT

The reliability and efficiency of NDT inspections have been improved by the use of computer aided systems and artificial intelligence techniques. Computers have become an essential tool in NDT for automated inspections, remotely operated systems, signal processing, signal interpretation and defect visualisation. Automated systems can operate in hazardous environments and provide inspection results of greater reliability than those from a human operator. Automated systems for MPI,13 eddy current,25 ultrasonic44'65 and radiographic inspection50,52 have been developed and are currently employed in a number of industries. Computer facilities allow 2-D and 3-D visualisation of flaws, improving the reliability and

Performance assessment ofNDT methods 73 accuracy of an inspection.26 The increasing use of computers in NDT (e.g. Lizard, ACFM, Andscan) has the major objective of minimising human intervention in terms of signal interpretation26,66'67 therefore enabling the inspector to concentrate more on the inspection and less on interpretation. Inspectors are subject to fatigue and loss of concentration and may work in a high risk environment; machine-driven NDT systems provide a means to surmount these problems and are cost-effective both as a short-term and as a long-term solution. Portable PCs are available at affordable prices and can be used to down-load onsite data sets which can then be processed at a later stage.66'68'69 Progress in artificial intelligence has contributed towards the development of artificial neural networks and expert systems for pattern recognition, signal interpretation and defect classification.27'45'67,70 They usually require broad database representatives of different types of signals and inspections. This can be a costly and time consuming phase in development and research. Expert systems emulate human reasoning by applying mathematical algorithms and logical inferences to a knowledge base for decision making purposes.71'72 Neural networks have been shown to be effective for classification of weld defects from ultrasonic images and for defect characterisation from ultrasonic and eddy current signals.28,73-76 NDT techniques producing a visual display, such as a radiograph, have benefited from developments in image processing and feature extraction using neural networks. As stated by Guettinger et al.,11 'Image processing assists the NDT technician during manual testing and lends itself to automation'. Digital signal processing (e.g. FFT, filtering, averaging) and image enhancement techniques of radiographs and ultrasonic images have been developed.78 Such systems find applications in the on-line manufacturing inspection of components. The combination of artificial intelligence and knowledge based systems has led to automation of NDT procedures. Computer visualisation is also a new feature of recent NDT equipment such as Andscan. The area of the component inspected is displayed on the screen of the computer and any defect is displayed in a colour coded manner.77'79 Defect location and quantification are greatly facilitated, reducing error in signal interpretation. Moreover, the images produced can be rotated, saved on disk for quality record purposes, and printed as hard copy documents. Multiprobe array scanning systems, of eddy current or ultrasonic inspection equipment, generate signals which can be imaged using commercial software such as Excel, PV-Wave or Dadisp (see chapter 4).26 Computer aided design (CAD) software has been coupled with visualisation of NDT data for complete and accurate defect characterisation.80,81 The combination of CAD, defect visualisation and finite element analysis could provide the NDT inspector with information on the structural integrity of a component by displaying areas prone to failure. Software has also been designed for NDT training and certification.82 Remotely operated vehicles (ROVs) are already in operation for underwater inspection of offshore platforms and can be more effective than human operators. Development of robotic systems for totally independent inspection, signal interpretation and decision making is still under way. Data fusion will contribute efficiently towards the achievement of such systems. 3.12

Performance assessment of NDT methods

Because the accuracy of inspection processes is uncertain, statistical and probabilistic methods to describe and compare the performance of NDT techniques have been developed.83"86

74 Non-destructive testing techniques 3.12.1 PROBABILITY OF DETECTION The concept of probability of detection (POD) is a statistical representation of the ability of a technique to detect a specific defect size. The use of probability of detection curves is becoming more frequent, especially in the aerospace industry.84,86 Figure 3.29 shows the change in probability of detection of a NDT method against the change in defect size. Several useful parameters can be defined from this curve. These are the defect detection threshold value, the sure defect detection value and the median defect detection value. The threshold value corresponds to the minimum detectable size of a defect, the sure detection value is the minimum defect size detected with a POD close to 100 per cent, and the median defect detection value is the defect size detected with a POD of 50 per cent

Detection

i

ii

I / / '

'

/

t

ii

i

/

0.5

ili

*

04

\t

i

* ii'

'/

1

• / ! / ; / \ '

Pro

cd X>

l

!

0.3 0.2

\ 1

1/ n

' .

0.9

/

/

I '

0.1

i

//

'

/

1

/

' Visual — — -Infrared Radiography Eddy Current

'

s - ^

•

1

.

2

.

3

,

,

,

,

4

5

6

7

,

,

9

10

Impact Energy / Joules

Fig. 5.17 Probability of detection vs. impact energy for different NDT methods sure detection value is the minimum defect size detected with a POD close to 100 per cent and the median defect detection value is the defect size with a POD of 50 per cent (Table 5.2). The impact detection threshold value for eddy current inspection is five times better than that for inspection by infrared or X-ray. Moreover, impact energies above 0.27 J can be detected with a 50 per cent confidence level using eddy current against 0.5, 1.6 and 3.0 J for radiographic, visual and infrared inspection respectively. The POD curves show the high performance of eddy current compared to visual, infrared and radiographic examination for low energy impact detection in carbon reinforced composites. Table 5.2 POD parameters from Fig. 5.17 for different NDT methods POD parameter

Visual examination

Infrared inspection

Radiographic inspection

Eddy current examination

Threshold value/J Median detection value/J Sure detection value/J

0.5

1.5

0.3

0.1

1.6

3.0

0.5

0.27

8.0

8.5

6.5

5.0

Figure 5.18 is a plot of POD against impact energy for different sensitivities of the eddy current instrument; sensitivities used varied from 5.0 V/div (volts per division) to

118 Bayesian approach to inspection of composite materials 0.21 V/div. The sensitivity of the apparatus needed to be adjusted carefully in order to select the most appropriate level for the material inspected (Fig. 5.19). It can be seen from Fig. 5.18 that the best results are obtained with the highest sensitivity at 0.24 V/div. However, other factors such as the probability of false calls, which do not appear in POD curves, have to be taken into account to assess the performance of a system. False calls are considered when performing receiver operating characteristic analysis as described in the following section.

^ ^ ^

S

r*"

0,9 -f

•7 it / it

\

o

0,6

un // //

/ 25

S2

t •? /

u/ 1 \i

1 J

S1

I

S0.5

/

Vi / 0,1

•

V

S0.2

/

f1 PT 1 1 1 . M M

1

Sensitivities II 1 M I I M ' 1 I I i i 11 1i i 1 1 1 1 1 1 11 M 1 11 M 1 I I 1 M 1 1! I1 I 1I 1I I I I I I I I i i I I I I I I I I I 1 1 M 1 i 1 M M II

2

3

4

5

6

7

8

9

10

Impact Energy/J

Fig. 5.18 Probability of detection vs. impact energy for different sensitivities of the eddy current instrument used for inspection of sample 1

Aeronautical products have to be inspected at the manufacturing and in-service levels for safety and economic reasons. Certain flaws need to be detected at an early stage and their growth monitored before they reach a critical size. As has been shown in the previous experiments, eddy currents are well suited to the detection of low energy impacts in composite materials. Visual inspection provides only limited information for low impact energy defect detection but is useful for a rapid check and estimation of a damaged area. Infrared thermography was not efficient in the detection of impact energies of less than 2.0 J. The IRT technique appeared to be as efficient as visual inspection. The resulting radiographs showed that X-rays are not sensitive to planar defects such as impacts, and that only broken fibres can be detected. One of the limitations of the POD representation is that it does not display the number of false calls of a technique. In order to take false calls into account, a receiver operating characteristic analysis is performed (see section 5.7.3) on the data obtained for eddy current inspection at different sensitivities.

flenrntiyitY 3.0 V/div.

Sensitivity 6.0 V/dlv.

0

6

O

v

o

0

0

0

o

O

O

• c=

•o

0

:

o 6 o

flenrttivity 1.0 V/div.

o o o flenrttivity 0.6 V/div.

0

0



(6.1)

The Gaussian normal distributions for the length and depth for both systems for the calibration slot 40.28 mm long and 1.96 mm deep are given in Fig. 6.8. One can see from these graphs that both systems are in agreement regarding the defect depth. It also appears that system B has more support than system A for the depth estimation. Regarding defect length, both systems appear to be in disagreement, with system A presenting a high and narrow probability density function (therefore with a smaller standard deviation) and system B with a much lower support regarding the defect length. Prior to data fusion and without the use of MPI, no conclusion can be made regarding which system gives the best length estimate. It can be seen from the graph that system A will always have the strongest probability function regardless of the accuracy of system B. The normal probability distribution graphs for the depth of the calibration slots can be

l l — System A . . . System B 1

0.5

/ *

I-

.System A - . . System B I

0.4 0.3

>Wn

m

| 1

Jl

*\

// ^f.'

'-Sw

1.5

2

2.5

Defect Depth / mm

0.2 0.1 0

-H—,—,—,—,—,—,7 \ , V . ,—J 4.5

9.5

14.5 19.5 24.5 29.5 34.5 39.5 44.5 49.5 54.5 59.5 Defect Length / mm

Fig. 6.8 Normal probability density function for systems A and B for a 40.28 mmx 1.96 mm calibration slot

Non-destructive examination of the test specimens 133 transformed into the standard normal distribution by means of the equation x-x z=

(6.2)

a

and the function O(z) can be plotted:

M"

••'•

HAZ crack

Fig. 6.35 Schematic defect position on sample tested

Fig. 6.36 View from above of the eddy current inspection of the sample described in Fig. 6.35 using system A

Fig. 6.37 View from above of the eddy current inspection of the sample described in Fig. 6.35 using system B

NDT data fusion

173

classification and geometrical transformation. Results from this approach are presented in Table 6.43. Pixel level data fusion results can be assessed using colour intensity, with bright red and yellow representing high probability defects and lighter red and yellow representing a low support towards defect indication. From the results in Table 6.43 it was noted that direct image addition does not improve decision making as even false calls are combined. The multiplication operation tends to increase the support of identical features such as toe and HAZ cracks but to the detriment of length information. The subtraction operations had the effect of reducing length information and the false toe crack was still visible. Direct mathematical operations on raw images from two eddy current systems do not really aid in decision making. Another approach investigated was to perform the same operation but using weighted images. The weight associated with each original image was selected from the previous weld inspection. Results of these operations are summarised in Table 6.44. The pixel level data fusion of information using images weighted with probability collected from real inspections improve pixel fusion outcome. The best results are obtained with the addition and multiplication operations. The output of the addition operation is a high degree of support for the HAZ crack and the toe crack without any false indication. The result of the multiplication operation appears more realistic and presents interesting information. There is a high degree of support in favour of the toe crack but it still appears intermittent; an intermediate degree of support for the HAZ crack; and a very low degree of support for the false toe crack. This is a good approach as both real defects are visible and have been detected, and any potential defects are displayed but with a low

Table 6.43 Mathematical operations for fusion of eddy current data at pixel level Image operation

Comments

Original image from system A Toe crack and HAZ crack detected Defect location + dimension correct Original image from system B Toe crack detected but intermittent HAZ crack detected but longer than actual length False call (extra toe crack detected) All defects detected with systems A and B are Addition present including false toe crack Increase in support regarding the presence of actual toe crack and HAZ crack but no improvement on length information or intermittent toe crack Multiplication More certainty on the presence of the actual toe crack Reduction in support regarding both the HAZ crack and the false toe crack Subtraction Actual toe crack and HAZ crack visible Toe and HAZ crack lengths smaller than actual length False toe crack still present

174

Implementation ofNDT data fusion to weld inspection Table 6.44 Mathematical operations of weighted images for fusion of eddy current data at pixel level Image operation

Comments

Weighted image A Actual toe crack and HAZ crack detected Defect location + dimension correct Stronger support Weighted image B Toe crack detected but appears intermittent HAZ crack detected but longer than actual length False call (extra toe crack detected) but less support associated with it Toe crack and HAZ crack with high support Addition Correct location and length No false toe crack visible Toe crack and HAZ crack with high support Multiplication False toe crack almost cancelled More certainty towards information from system A, even so toe crack appears intermittent High support for the HAZ crack, correct length Subtraction High support for toe crack but smaller length Very low support for false toe crack

degree of support which means that they are not neglected as with the addition operation. Again there is more confidence in the measurement from system A. This is shown by a higher confidence level for the estimated location and length of the HAZ and toe cracks, and by a lower confidence level for the false toe crack detected with system B. Combination of eddy current and ultrasound data With the fusion of images from eddy current and ultrasonic inspection, an increase in the knowledge of the actual total number of defects present on the same sample is expected. Because both techniques are complementary, eddy current inspection should detect mainly surface breaking defects and ultrasonic testing should detect internal defects, so the logical operation AND was chosen. This has the result of directly adding both types of defects on the same image. The surface breaking defect detected with eddy currents (toe crack) and the internal defects detected with ultrasound (slag inclusion) can be represented and made visible on the same image to facilitate structural assessment by fracture mechanics specialists. An increase in the spatial observation domain can therefore be achieved. 6.4.7 PERFORMANCE ASSESSMENT OF THE BAYESIAN AND DATA FUSION PROCESSES

DEMPSTER-SHAFER

In order to determine the optimal decision-fusion process, the performance of the Bayesian and Dempster-Shafer approaches to NDT data fusion should be assessed. In

NDT data fusion

175

the previous sections the decision output from each data fusion process was compared to the actual and expected defect depth or length. In this section, receiver operating characteristic (ROC) curves are used to establish a measure of the decision making associated with depth estimation from the fusion of eddy current data by each data fusion process. 1314 Assessment of the decision making associated with length estimation was not possible due to the large difference in sensor efficiency between system A and system B (section 6.4.1). Receiver operating characteristic curves for the Bayesian and Dempster-Shafer data fusion approaches regarding decisions associated with depth estimation from the measurements on calibration slots are shown in Figs 6.38 and 6.39. A binary decision rule to determine whether or not depth estimation could be assumed accurate was used. A measurement of the accuracy of the Bayesian and Dempster-Shafer approaches was performed by calculating the area under the ROC curve using a trapezoidal rule.15 Figure 6.40 is a measure of the area for depth estimation of the Bayesian and Dempster-Shafer approaches from the fusion of eddy current measurements of calibration slots. According to the area under the ROC curves for each calibration it can be said that the Dempster-Shafer evidential theory is more accurate, regarding decision making of depth estimation from NDT data fusion, than the Bayesian statistical inference process. Therefore, the use of the Dempster-Shafer theory would be preferable to the Bayesian theory in terms of accuracy of depth estimation resulting from the combination of eddy current data.

Fig. 6.38 Receiver operating characteristic (ROC) curves of the Bayesian data fusion approach at depths 1.96 mm (a), 1.00 mm (b) and 0.53 mm (c) on calibration slots

Fig. 6.39 Receiver operating characteristic (ROC) curves of the Dempster-Shafer data fusion approach at depths 1.96 mm (a), 1.00 mm (b) and 0.53 mm (c) on calibration slots

100

1.96

1.00

0.53

Calibration Slot Depth / m m

Bayes

Dempster-Shafer

Fig. 6.40 Measured values of area under ROC curves for decision making of depth estimation with the Bayesian and Dempster-Shafer data fusion approaches

Discussion

111

6.5 Discussion Fusion of NDT data from eddy current and ultrasonic systems applied to weld defect characterisation using the Bayesian inference theory and the Dempster-Shafer theory is presented in this chapter. Quantitative and qualitative data fusion at a statistical level and pixel level is performed. Finally a comparison of the two data fusion processes implemented is carried out using ROC curves. From this study it was noted that data association through Bayes's theorem is adequate to provide a measurement of certainty about a hypothesis and to make inferences. The Bayesian approach is very dependent upon sensor efficiency and knowledge of a measurement and does not always allow decision making, particularly in the case of length estimation. It was also demonstrated that an increase in the number of sensors for a specific task would not only reduce inspection time, but also increase the performance of a data fusion system. An increase in accuracy can be achieved with the Dempster-Shafer approach by presenting data in the form of an interval. The Dempster-Shafer evidential theory appears to be more adequate when information from multiple systems is combined, and does not require prior knowledge of a measurement. The Bayesian inference theory updates the probability of hypotheses and allows multiple hypotheses to be evaluated simultaneously, while the Dempster-Shafer evidential reasoning can evaluate only two hypotheses at a time. The Dempster-Shafer theory updates an a priori mass density function to obtain an a posteriori evidential interval. The evidential interval quantifies the belief of a proposition and its plausibility. In some cases an improvement in decision making was achieved from the fusion of NDT data. However, in all instances, mistakes were made which resulted in no, or inaccurate, decision outputs. It was important to identify the causes of errors in order to determine the actual limitations and advantages of each data fusion process and to prevent similar errors from happening again in future experiments. Errors in fusion output do not appear to be dependent upon defect type; however, defect detection clearly depends upon defect type, as root cracks and intermittent cracks were not detected with eddy current system B. Combination of length measurements from eddy current systems A and B using the Bayesian approach did not help in decision making, as prior to fusion, system A was too efficient compared to system B. Due to the low belief associated with measurements from system B, information from system B was discarded at the fusion level. In this particular case, the increase in sensor number did not produce an increase in sensor efficiency. This demonstrated that NDT data fusion will ameliorate measurement accuracy only if there is a need for improvement. The system with high sensor efficiency will always have more belief associated with its signal output than any other system. Performance assessment through ROC curves showed that the Dempster-Shafer data fusion approach was more accurate than the Bayesian statistical inference theory. To be truly objective, performance of both data fusion processes would have to be compared with results from depth estimation performed by experienced operators during field inspections who do not have information about the location and size of any defects. It was also seen that the combination of information from three different NDT methods, namely eddy current, ultrasound and radiography, does not necessarily have the effect of increasing the belief associated with a measurement. This is mainly related to the input values from each NDT method considered; little knowledge associated with one NDT method has the effect of increasing the uncertainty of the overall fused information.

178 Implementation of NDT data fusion to weld inspection An increase in the spatial observation domain was achieved from the combination of ultrasonic and eddy current data at pixel level. Pixel level data fusion can be useful to gather information from different types of sensors but data visualisation is required prior to fusion. Applying Bayesian, Dempster-Shafer or fuzzy logic rules to pixel level data fusion may present interesting results and this is certainly a viable alternative, especially with the increasing use of digital output from NDT equipment. No data fusion system can produce a 100 per cent accurate decision output but it can certainly improve decision making by providing operators with a degree of certainty associated with a measurement. One could ask the question: 'Is NDT data fusion worthwhile?' The operations described in this chapter have shown that no two NDT systems provide identical information regarding a defect size or location. However, the combination of information from multiple systems helped in decision making and increased accuracy. Therefore, it can be said that NDT data fusion will greatly improve accuracy in defect characterisation and will definitely: • • • • • •

increase confidence level, increase spatial observation domain, reduce ambiguity, improve defect detection range, enhance NDT system reliability, and improve the overall performance of a non-destructive examination.

References 1. Dover WD, Rudlin JR. Underwater inspection reliability trials, 13-16 Oct. 1992, International Offshore Conference and Exhibition, Aberdeen, UK. 2. The normal distribution, 1984, The Open University Press, Unit 9. 3. Caulcott E. Significance tests, 1973, Routledge & Kegan Paul. 4. Waltz E, Llinas J. Multisensor data fusion, 1990, Artech House. 5. Gibbons JD. Nonparametric statistical inference, International Student Edition, McGraw-Hill, 1971. 6. Grox XE, Strachan P, Lowden DW. A Bayesian approach to NDT data fusion, May 1995, Insight, 37(5), 363-7. 7. Lindley DV. Introduction to probability and statistics from a Bayesian viewpoint Part 2: Inference, 1970, Cambridge University Press. 8. Rudlin JR, Wolstenholme LC. Development of statistical probability of detection models using actual trial inspection data, Dec. 1992, British Journal of Non Destructive Testing, 34(2), 583-9. 9. Gros XE, Strachan P, Lowden D. Edwards I. NDT data fusion, Oct. 1994, Proceedings of the 6th European Conference on Non Destructive Testing, Nice, France, 1, 31-5. 10. Gros XE, Strachan P, Lowden D. Theory and implementation of NDT data fusion, 1995, Research on Non Destructive Evaluation, 6(4), 227-36. 11. Georgel B, Lavayssiere B. Fusion de donnees: un nouveau concept en CND, 24-28 Oct. 1994, Proceedings of the 6th European Conference on Non Destructive Testing, Nice, France, 1, 31-5. 12. Johannsen K, Heine S, Nockemann C. New data fusion techniques for the reliability

References 179 enhancement of NDT, Oct. 1994, Proceedings of the 6th European Conference On Non Destructive Testing, Nice, France, 1, 361-5. 13. Swets AJ. Measuring the accuracy of diagnostic systems, June 1988, Science, 240, 1285-93. 14. Nockemann C, Tillack GR, Wessel H, Heidt H, Konchina V. Receiver operating characteristic (ROC) in non-destructive inspection, Aug. 1993, Proceedings of the NATO Advanced Research Workshop 'Advances in Signal Processing for Non Destructive Evaluation of Materials', Quebec, Canada. 15. Pete A, Pattipati KR, Kleinman DL. Methods for fusion of individual decisions, June 1991, Proceedings of the 1991 American Control Conference, Boston, IEEE Cat. No. 91CH293-7, 2580-5.

7

Perspectives of NDT Data Fusion The use of diverse methods, in concert, to solve data fusion problems is still evolving DL. Hall, 1992

7.1

Concluding comments

The most common data fusion methodologies, theoretical and experimental approaches to the concept of NDT data fusion, are presented in this book. The identification of data fusion algorithms and of the factors that contribute to the reliability of NDT data fusion are discussed. The design of a data fusion system for industrial applications is described. This chapter completes the work presented on multisensor data fusion by summarising the material from earlier chapters and making concluding remarks on the theory and application of NDT data fusion. Future research directions and possible applications are also presented for the further development of multisensor NDT data fusion. Application of NDT data fusion, described in chapters 5 and 6, showed that fusion should be performed using data which is as close as possible to the original data. This has the effect of minimising loss of information, which could occur from extensive signal processing, and reducing complex processing operations. The criteria for the design of a data fusion model are identified as maximum simplicity, maximum efficiency and operational flexibility. Unfortunately the last point is not a synonym for simplicity. Raw sensor data constitutes the most common input format of data integration and fusion systems; however, if a multi-usage compatible system is required, other parameters have to be considered. Among these are information on sensor location, information on external databases and environmental data, as well as information related to the experience of human operators (Fig. 7.1). Therefore very complex data management would be required to develop such a powerful system to perform the following operations: • • • • • • •

store data in multiple format; access and modify data format if required; allow interactive access of data by multiple users; have a user friendly interface; be compatible with other systems; be secure to prevent unauthorised access to the data; be able to compress data for optimisation of storage space.

It is clear from the research presented in this book that multisensor NDT data fusion is

Defect Detection

I

Defect Quantification

1

I

i

I

NDT Techniques

,

X

I

Location Orientation Length Depth

Visual MPI

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.etc.

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,

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x

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x

Material Dimensions Access On site

Inspection Procedure

l Laboratory

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Manual

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x Qualification Experience Age

Fig. 7.1 Factors to consider in the development of a data fusion system for multiple uses

182 Perspectives of NDT data fusion worthwhile and can improve the overall efficiency of a non-destructive examination. It has also been demonstrated that NDT data fusion could be applied to different types of inspections, regardless of sensor types. The data fusion algorithm implemented took the form of probabilistic inference processes such as the Bayesian inference theory, the Baysian estimate and Dempster-Shafer evidential reasoning. Data fusion can be performed at the signal level using raw eddy current data collected during the inspection of composite materials and with data which characterise defect size (depth or length) from eddy current and ultrasonic sensors used for weld inspection. A pixel level data fusion approach with signals from eddy current and ultrasonic systems applied to weld inspection has also been used. It was demonstrated that data fusion can help to make an inference about a hypothesis both in the case of binary decision making, such as for defect/no defect1, and in the case of quantitative information, such as defect depth, from more than one NDT system.2 From the experimental results achieved, the Dempster-Shafer approach was preferred as it is more efficient than the Bayesian approach in making accurate estimations of defect depth. It also presented the results with associated probability intervals which were used to make decisions in favour of a data fusion output with the highest degree of confidence. The outcome of the Bayesian inference process is a posterior probability which either supports or refutes a hypothesis. This type of reasoning is useful for binary testing and provides a measure of uncertainty of a hypothesis. In the case of non-binary testing, the Dempster-Shafer evidential reasoning is better suited to making inferences. In both cases, an increase in sensor number would not only reduce the inspection time but also increase the performance of the fusion system. It was also noted in chapter 6 that the final decision outcome is affected by the performance of each NDT system. Improvements from data fusion would only be achieved if each NDT system is in a similar performance range. No advantage would be gained from the fusion of NDT systems with poor performances as the belief associated with any sensor output would be very low. As stated by J.W. Tukey (1977), 'We have not looked at our results until we have displayed them effectively'. Visualisation of NDT data was performed using commercially available software which provides an affordable way to display data in a format which has colour coded images. This allows rapid location and sizing of defects as illustrated in chapter 5, with the detection and visualisation of disbonds and impact damage in composite materials. For NDT data fusion, visualisation enabled clarification of many aspects of defect detection by presenting data fusion results in a format which facilitates interpretation and also enables pixel level data fusion. Similar displays could be used as inputs to neural networks to assist the human operator in decision making by performing pattern recognition tasks as well as pixel level data fusion operations. Prior to the implementation of NDT data fusion, the most common NDT methods were described and their physical principles studied before carrying out experimental inspections. This brief study is necessary to select complementary NDT methods, the data from which could be combined at a later stage. The use of eddy currents to detect and quantify defects in composite materials was also investigated. This study demonstrated that electromagnetic techniques were a low cost, highly reproducible and efficient alternative to ultrasound and infrared thermography for inspection of composites. Standard statistical analyses such as POD and ROC were used to assess the performance of the eddy current system. Similar statistical analyses were carried out to assess the performance of the data fusion algorithms implemented, which demonstrated - in the experiments described in chapter 6 - the Dempster-Shafer evidential theory to be more accurate regarding decision making of defect depth estimation than the Bayesian theory.

The future ofNDT data fusion

183

From the comment by R.A. Armistead, 'No single NDT method alone can provide a total solution to the needs of the advanced engineering materials community', the use of multiple NDT techniques is anticipated, leading to a need to display and combine data effectively. Fusion of NDT data can be used to combine information from multiple identical or different sensors and to make inferences on inspection results. The technology is already available to perform such a task but only a direct requirement by industry would promote further development of NDT data fusion. Research has already started at an industrial level and it is not surprising that this need has come from the nuclear industry for which the use of multiple NDT methods is necessary to meet safety standards. More research is required to develop an NDT data fusion system for on-site inspections, to develop a database and a man-machine interface and to configure a system for a specific application. Configuration and interfacing of communication software for data collection and data transfer, and of visualisation software for data display, mapping and analysis would be required. A data fusion algorithm adapted to the problem would have to be selected or especially designed, depending on the data format and application. The statistical approach to NDT data fusion described in this book demonstrated that an improvement in defect characterisation can be achieved by combining information from multiple sensors. Two approaches, based on the Bayesian and the Dempster-Shafer theories, were implemented for NDT data fusion. From these two approaches, it was noted that the Bayesian posterior probability tended to be more affirmative than the support generated by Dempster-Shafer, because in the latter, when there is conflicting information, uncertain results are obtained. The Bayesian inference process provides a probability of a hypothesis being true when given evidence; however, the prior probabilities are highly dependent on experimental results. Unlike the Dempster-Shafer approach, there is no uncertainty associated with the decision towards a hypothesis resulting from a Bayesian calculation. However, the Bayesian approach is more appropriate for binary testing with multiple identical sensors than the Dempster-Shafer theory. Owing to the difficulty in defining prior probabilities, the Bayes estimate approach appeared to be best suited to making inferences towards measurement of an unknown quantity, i.e. a defect depth or length. It was noted that the Bayes estimate has the advantage of producing an estimated defect depth when given evidence from multiple sensors of which a normal prior probability on ju and o can be assumed. The Dempster-Shafer process is highly dependent upon the sensor efficiency; a small change in input data can produce a large variation in the outcome of the Dempster-Shafer rule of combination. For example a 55 per cent increase in accuracy for length estimation can be achieved with the Dempster-Shafer rule of combination when combining length information from instruments in relative agreement. But a variation of 1.00 mm of length measurement from one instrument can produce a change in the output, and an increase of only 11 per cent in accuracy is achieved. Combining similar information using the Bayesian approach produces an increase in accuracy of 38 per cent in both cases, regardless of the sensor variations. The advantages and limitations of both approaches are summarised in Tables 7.1 and 7.2. The data fusion process is dependent on the type of defect detected and the equipment used. Data input is in the form of length or depth measurements, and preprocessing of the original signal into a common numerical format is required to build a data fusion engine which will be able to combine information at the signal level. A large amount of experimental data needs to be collected prior to fusion, on multiple test samples and with several NDT instruments, in order to be able to build a database to assign prior probabilities to each measurement. However, once this set of data has been collected, data

184

Perspectives of NDT data fusion

fusion could be performed in real time using a computer program specifically designed for a particular inspection procedure. A schematic for the development of an NDT data fusion engine is presented in Fig. 7.2. Table 7.1 Advantages and limitations of the Dempster-Shafer data fusion approach Advantages

Limitations

Provides a soft decision output Associates belief and uncertainty values to a decision output Better accuracy than Bayesian approach for depth estimation (5% more accurate)

Poor results if both systems are in relative disagreement Small changes in input data can produce important changes in decision output The estimated defect depth is given as a depth interval, not a definitive depth value

Table 7.2 Advantages and limitations of the Bayesian data fusion approach Advantages

Limitations

Best suited for binary decision testing Provides only a hard decision output (no uncertainty values) Low computational requirements Prior probabilities can be difficult to obtain (Bayes estimate is preferable) Output highly dependent on sensor efficiency Sensors need to be of similar efficiency (not suitable for combining information from sensors with high discrepancy)

7.2

The future of NDT data fusion

From the progress achieved in the fields of NDT data visualisation, artificial intelligence, pattern recognition, NDT data fusion and discussions with industries in need of an efficient and reliable NDT technique, a rapid evolution of the concept of NDT data fusion is foreseen.3"5 It is more economically viable to keep using conventional NDT equipment and combine data collected from such systems than to redesign existing apparatus. One single NDT instrument can be used for multiple inspection purposes and data produced combined using a data fusion system designed for a specific NDT task. Pattern recognition and signal processing are already used to build expert systems to detect and classify defects with minimum operator intervention. In a highly advanced NDT data fusion system, the expertise of the human operator could be coupled with information provided by a machine, and expert systems developed or adapted to represent this knowledge and make inferences. The development of a neural network could have four major advantages: • • • •

to estimate the certitude of the sensor output (NN input) for different NDT systems; to extract significant information from each system; to extract relevant information from each system in relation to the type of inspection; to indicate a decision in a numerical or graphical format easy to interpret by an operator and ready for fusion.

J Identify Defect Location Defect Type Elements

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Fig. 7.2 Schematic for development of a NDT data fusion engine

Decision

186 Perspectives of NDT data fusion A three-level expert system could be designed to assist in non-destructive examinations (Fig. 7.3). Level 1

Level 2

—> NDT Sensor 1 — • Data Alignment Data Fusion i i Centre — • i Signal Processing NDT Sensor n —•

i

r

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Human-Machine Interface

T Decision Fig. 7.3 Design of an expert system to assist in non-destructive examinations Signal processing, data alignment and correlation are performed at the first level while data fusion operations are carried out at level 2 using conventional or specific data fusion algorithms such as Bayesian, Dempster-Shafer theories or fuzzy logic. At level 3, an expert system is used for situation assessment, identification of defect type, estimation of defect size, location and orientation. Other tasks such as failure analysis can be performed and information collected from an inspection compared to that of pre-existing information stored in a database. A human-machine interface analyses equipment malfunction and displays signals from NDT systems in an analogue, digital, 2-D or 3-D colour coded format, as well as displaying information from the data fusion centre in the form of images and/or statistical and probabilistic numerical values. The operator is also informed of the output of the expert system operations and of any major danger which may be associated with the presence of a defect. The final decision is left to the human operator but could be automated if required. Fully automated inspection using a remotely operated vehicle (ROV) for testing in hazardous environments has already been developed for the nuclear and offshore industries. By fitting multiple NDT sensors to an ROV, automatic logging of data, display of remote information in a safe environment and real-time data fusion could be performed (Fig. 7.4). Instrument and operator errors are significant factors in sizing and location of flaws. The knowledge and expertise of a human operator could be coupled with information provided by a machine in order to reduce human error. Artificial systems could be developed further to represent this information and to make inferences. Nuclear plants already have supervisory control configurations where decision support systems using Dempster-Shafer theory aid human operators to perform high level tasks by processing and displaying uncertain information.6 The final phase of this approach could be the adaptation of multiple NDT systems on remotely operated vehicles for completely automated inspection.

References 187 Safe Environment Data Logging

Data Visualisation

Data Fusion

Ultrasonic Sensor

Eddy Current Sensor

Underwater Environment Fig. 7.4 Schematic diagram of a fully automated ROV inspection

References 1. Gros XE, Strachan P, Lowden DW. A Bayesian approach to NDT data fusion, May 1995, Insight, 37(5), 363-7. 2. Gros XE, Strachan P, Lowden D. Theory and implementation of NDT data fusion, 1995, Research in Non Destructive Evaluation, 6(4), 227-36. 3. McNab A, Dunlop I. A review of artificial intelligence applied to ultrasonic defect evaluation, Jan. 1995, Insight, 37(1), 11-16. 4. Kirk I, Lewcock A. Neural networks - an introduction, Jan. 1995, Insight, 37(1), 17-24. 5. Windsor CG. Can we train a computer to be a skilled inspector?, Jan. 1995, Insight, 37(1), 36-49. 6. Hasegawa S, Inagaki T. Dempster-Shafer theoretic design of a decision support system for a large complex system, 1994, Proceedings of Institute of Electrical and Electronics Engineers International Workshop on Robot and Human Communication.

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Glossary

Accuracy: the extent to which an estimated or measured value approaches the actual true value (related to systematic errors associated with an experiment or an instrument). Acoustic emission testing: a technique which enables the detection of flaws by monitoring acoustic signals caused by plastic deformation of structures. Acoustic impact testing: a technique which uses variations in sound from the tapping of an object on a surface to detect surface anomalies in components. Algorithm: a set of rules which specifies a sequence of actions to be taken to solve a problem. Each rule is precisely and unambiguously defined so it can be carried out by a machine (computer). Alpha particle: (a) a positively charged particle emitted in radioactive decay of gamma isotopes. Alternating current magnetisation: magnetisation of a material induced by a magnetic field generated by an alternating current. Angle ultrasonic transducer: a sensor which transmits ultrasonic energy at a specific angle to the surface of a component. Arc strikes: burn damage to a material caused by the breaking of an active electric circuit. Array sensor system: a group of sensors combined in one system to reduce measurement time. A-scan: a cathode ray tube image which displays signal amplitude against sweep time. A-to-D converter: an apparatus which converts an analogue signal into a digital signal. Bayesian statistical inference: a decision rule used to make probabilistic inference about hypotheses. Beam spread: the divergence of an ultrasonic wave traversing a medium. Brittle fracture: rupture in a material without prior plastic deformation. B-scan: a 2-D image of the cross-section of a component inspected. Calibration slots: artificial slots or defects manufactured in a standard material to calibrate an instrument prior to inspection. Coercive force: the magnetic field strength required to reduce remanent magnetism to zero.

190 Glossary Coil: a conducting material shaped in the form of one or multiple loops which can induce magnetic fields when conveying an electric current. Convection: term used to describe the transfer of heat due to temperature differences. Conversion screen: a screen used to convert incident photons in another form of energy. C-scan: a 2-D plan of the scanned surface of a component inspected. Curie: an international unit of the rate of radioactive activity (1 Ci = 3.7 x 1010 disintegrations per second). Dead zone: the zone after an ultrasonic pulse where additional echo cannot be detected. Defect: a flaw or discontinuity in a material which may affect its structural integrity and/or may make it unsuitable for the task it has been designed for. Diamagnetic material: material repelled by a magnet and with a magnetic permeability of less than 1. Ductile fracture: a break in a material which has undergone plastic deformation. Eddy current examination: detection and quantification of surface and sub-surface flaws through measurement of the variations in an electric current induced by a time-varying magnetic field into the material inspected. Edge effect: phenomenon which causes signal distortion when a probe approaches the edge of a sample. Electromagnet: a ferromagnetic material which behaves as a magnet when the coil surrounding it is energised by an electric current. Electron: a negatively charged subatomic particle of 1.602 x 10"19 coulombs. EM AT: apparatus generating ultrasonic, horizontally polarised shear waves from a coil excited by an AC placed close to the surface of a conductive material. Far field: (also known as Fraunhofer zone): the distance at which the decrease of ultrasonic signal amplitude is inversely proportional to the distance of the surface of the material inspected from the sensor. Ferromagnetic material: a material whose magnetic resistivity and magnetic permeability are high and depend upon the strength of the magnetising field (e.g. iron, nickel, cobalt). Usually exhibits the hysteresis phenomenon. Fill factor: a term used to describe the level of electromagnetic couplage occurring between a test coil and the material that surrounds it. Fuzzy logic: a theory developed to quantitatively express imprecision between categories in the form of membership functions. Gating: the process of selecting a portion of a signal on account of time, magnitude or phase. Gauss: a unit of magnetic flux density. Hall effect: a change in voltage which occurs at right angles to the direction of the electric current and the magnetic field in a conductor stimulated by an electric current.

Glossary 191 Heuristic programme: a programme which attempts to improve its own performance as a result of learning from previous actions within the programme. Heuristic rules: approach based on commonsense rules and trial and error rather than comprehensive theory. Holography: an optical imaging process in which reflected light from an object is captured on photographic film without the use of a lens. Hue: the characteristic in which a colour can be classified as red, green, blue, etc. Hysteresis loop: a closed curve formed by plotting the magnetic flux density B versus the magnetic field H. IACS: a standard conductivity measurement in which the conductivity of the unalloyed copper is set at 100 per cent. Image processing: technique used to filter and enhance the quality of an image. Image quality indicator: a small reference specimen radiographed with the specimen under test to ensure the quality of a radiograph. Image segmentation: refers to the division of an image into multiple regions. Specific parameters representative of a scene are used for selection of each region before segmentation occurs. Such parameters are image intensity, texture, colour, spatial arrangement, shape and geometry. Impedance: the resistance of a material to the passage of an electric current. Inductance: the magnetism produced in a ferromagnetic material by an external magnetising force. Infrared radiation: the region of the electromagnetic spectrum associated with heat transfer. Intensifying screen: metallic or fluorescent screen used to convert incident X-radiation into light energy or electrons. Knowledge-based system: a software system composed of a knowledge-based module and an inference engine used to assist in decision making. LASER (Light Amplification by Simulated Emission of Radiation): an apparatus which produces a beam of coherent light. Leak detection: a technique which consists of injecting a search gas into a sealed enclosure and monitoring loss of gas to check for leaks. Lenz's law: if an emf is induced in a material, an electrical current is created which flows in a direction which tends to oppose the cause of the induced emf. Lift-off: a term used to describe the distance between the test coil and the test object. Liquid penetrant testing: a method of inspection where the surface of the component to be tested is covered with a visible penetrating liquid which concentrates in cracks. Magnetic field inspection: a technique used to detect surface and sub-surface defects by monitoring the variation of a magnetic field induced in the material tested.

192 Glossary Magnetic permeability: the ease with which a magnetic field can be induced in a material. Magnetic susceptibility: the amount by which the relative magnetic permeability of a medium differs from unity. Magnetising force: the force used to create a magnetic flux in a magnetic circuit. Maxwell's equations: fundamental equations of electromagnetic field theory. Measurand: the physical quantity measured by an instrument. Microwave testing: the detection of microwave radiation directed onto a test component is used to check the presence of flaws in composites. MPI: a NDT technique in which the component tested is magnetised and magnetic particles are sprayed onto its surface to reveal cracks. Multifrequency system: apparatus capable of generating more than one frequency in a sequential or simultaneous manner. Multi-layer perceptron: (also known as feed-forward network): a type of neural network in which the nodes are arranged in layers. It is composed of an input layer, an output layer and any number of hidden layers. Multisensor data fusion: a theory which can be described as the synergistic use of information from multiple sources to assist in the overall understanding of a phenomenon, and to measure evidence or combine decisions. Near field zone (also known as the Fresnel zone): disturbance zone after the initial ultrasonic pulse in which defects cannot be sized or detected. Neural network: computer system designed to produce a set of output values from a set of input data. Non-ferromagnetic material: a material into which a magnetic field cannot be induced. Paramagnetic: a phenomenon in some materials in which the susceptibility to magnetism is positive and the magnetic permeability is slightly higher than unity and independent of the magnetising force. Precision: related to the random error distribution associated with an experiment or an instrument. Prods: hand-held electrodes used to pass magnetising current through a material. Pulse-echo method: an ultrasonic method which uses back-echoes to detect flaws in components. Radiographic inspection: X-ray, gamma or neutron radiography techniques which use penetrating radiation to detect internal faults in components. Rayleigh wave: an ultrasonic wave which propagates at or near the surface of a material. Remanent magnetic field (also known as residual magnetic field): the magnetic field remaining in a ferromagnetic material after reducing the magnetising force to zero. Resolution: a measure of the capability of an instrument to distinguish between two signals at very small distances from each other.

Glossary 193 SAFT: ultrasonic technique based on the concept of collecting waveforms from a scanning transducer and processing them as a single unit. Sensitivity: the lowest limit of detectability of a signal, defect or detail on an image. Signal-to-noise ratio: the ratio of signal amplitude to noise amplitude. Skin depth: the depth at which the intensity of an induced eddy current has decreased to 37 per cent of the surface value. Skin effect: a phenomenon by which high frequency electrical currents tend to be concentrated in the thin layer of conductors. Skip distance: the distance from the point at which the ultrasound beam first enters the test specimen to the point at which the back-reflected pulse first encounters the front surface. Synergism: combination of the action of two or more sensors resulting in enhancement of the efficiency of a process. Thermal conductivity: a measure of the rate of heat flow through a given area and thickness in the presence of a temperature gradient. Thermography: technique which consists of mapping isotherms over a surface. TOFD: an ultrasonic technique based on measuring the time separating two diffracted waves from the defect extremities. Transmission: a physical process by which energy waves travel through a medium. Ultrasonic testing: a technique which uses variations in the echoes of ultrasonic pulses injected into a material to detect and size internal defects. Vibrothermography: a thermal inspection technique which uses cyclic vibrations to induce heat into a material. Visual inspection: an optical technique carried out with or without optical aids to inspect the surface of materials.

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Index

Acoustic emission 71 Acoustic impact 72, 98,107 Alternating current field measurement 58-59 Alternating current potential drop 57-58 Artificial intelligence 6,31-34, neural network 14, 32-34 Bayes cost function 24 criteria 24-25 risk 14,24 Thomas 25 Bayesian estimate 157-162 inference 22,25-26,95-126,114-116, 143-157,166,168-169,183-184 statistics 14 Boroscopy 44-45 Chi-squared test 133-135,139 Classical inference 22-23 see also impact damage Composite materials 95-96 CFRP 103-104 coin tapping 98, 107 computer tomography 107 -108 eddy current testing 100,104-107, 109-111 examination 104 GFRP 101 infrared thermography 99,108-110 inspection results 104—114 laser holography 100 NDT methods 97-101 panels 103-104 radiographic inspection 99-100, 110-111 shearography 100 Tedlar 112,122 ultrasonic testing 98-99 visual inspection 98, 107, 109-110

Data fusion see also NDT applications 34 centralised 15 definition 1, 5, 13 distributed 15-16 factors 181 flaw diagram 14, 142, 185 human 6 methodology 22-34 models 6,13-22 paradigm 14 performance assessment 174-176 pixel level 31-32,170-174 process 13-14 sensors 8-13 Data integration 13,114-121 Decision 22-31 binary 18-19 hard 142-143 output 17-19 probability 20-21 rate 18 soft 142-143 Delamination 102-103, 107-109 Demagnetisation 51 Dempster-Shafer 13, 17, 22, 26-30, 162-170, 184 belief function 27 data fusion 35, 162-170 decision 29 evidential interval 29,162-166 plausibility 29 rule of combination 28 Disbond 96-97,101-107 Eddy current bridge circuit 54 coil 52, 54-55 conventional 51-55 impedance diagram 53 multi-sensors 54-55 pulse 55,57 remote field 57

204

Index

Eddy current (Continued) skin depth 53-54 testing 51,100,105-107,109-112, 129-137 variables 55 Endoscopy see Boroscopy Expert system 186 Evidential reasoning see Dempster-Shafer Fuzzy logic 22,30-31 membership function 30 Gaussian normal distribution 132-133, 139 GEP theory 15,22,30 Helicopter rotor blade 101-109 Holography 45-46,100 Hypothesis multiple 20-21 testing 114-116 Image processing 89-91 Image segmentation 31-32 Impact damage 96-97,109-114 Infrared thermography 71,99,108-110 Introscopy see Boroscopy Kolmogorov-Smirnov test 135 -136,139 normal plots 136-137 P-values 135,139 Laser holography see Holography Leak detection 72 Likelihood ratio criterion 23-24 Liquid penetrant inspection 46-48 Magnetic field 48,51,58 flux density 48 flux leakage 48 hysteresis 49-50 particle inspection 48-51 Magnetising currents 51 Markov random field 22, 32 Maximum a posteriori 23 Microscopy 44-45 Microwave inspection 72 Multiple sensors 6-8, \6-\l see also eddy current combination 16-17 integration 13 output 17-22 parallel suite 16 serial suite 16-17 NDT computers 72-73

data fusion 34-36,95-96,107,114-116, 127-187,141-178 definition 1,43-44 expert systems 73,186 performance assessment 73-77 underwater 186-187 visualisation 73,91-92 Neural network see Artificial Neyman-Pearson test 24 Perceptron 32-33 POD 10,12-13,74-75,116-118 Probability 20-25,133,145 see also POD a priori 26 density function 132,138 posterior 148-162 prior 166 Radiography computer tomography 69-70, 100 107-108 electromagnetic radiation 67-68 gamma rays 66-68 geometric projection 66 geometric unsharpness 68 half value thickness 68 image formation 66, 68 inspection 66-71,99-100, 110-111 140-141 IQI 68 neutron radiography 69-70 principle 66-68 radiation intensity 68 real time radiography 69 X-rays 66-68 X-ray fluorescence 70 Receiver operating characteristic 10,12-13, 75-77,120-121,175-177 ROC see Receiver operating characteristic ROV 186-187 Sensor efficiency 17-20,129-132 errors 10—11 information 8-9 management 8-13 performance 10-13 selection 8-10 Shearography 72,100 SQUID magnetometers 71 Tomography see radiography Ultrasonic acoustic impedance 63 A-scan 59-60 attenuation 64

Ultrasonic (Continued) B-scan 60 C-scan 60 couplant 62 DGS 62 EMAT 65 far zone 62 frequency 59-60 near-field zone 60,62 principle 59-60 P-scan 60 pulse echo 59-60 reflection 63-64 SAFT 65 shear waves 63-64 testing 59-66,98-99,137-139 through transmission 59,60 time-of-flight 65 TOFD see time-of-flight transducers 60-62 transmission 63 velocity 63 wave propagation 60 wavelength 62-64 Vagueness 31 Visual inspection 44-46,98,109-110

see also microscopy, boroscopy, holography Visualisation animation 88-89 CAD 83,91 colours 84-86 data visualisation 83-87 definition 82-83 illusion 86-87 NDT 73,87,89,91-92 RGB 85 spreadsheet 84 tools 84 virtual reality 88-89 visual data analysis 83 volume 87-88 Weld eddy current testing 129 -137 heat affected zone 128,171 -174 inspection 127-179 lack of side wall fusion 140-141 NDT 129-141 radiographic inspection 140 -141 samples 128 toe crack 140,171-174 ultrasonic examination 137 -139

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Plate 1

Visualisation, using Excel spreadsheet, of eddy current data from the inspection of the surface of a helicopter rotor blade

Plate 2

Combination of 3-D colour coded variations in elevation on the surface of a helicopter rotor blade and contour map. Defects are shown in red and yellow

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Plate 3 Typical ACFM signal display from a defect (Bx and Bz plots and butterfly plot are respectively displayed on the left and right of the screen)

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Plate 4

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Multiple sequences from a computer animation procedure showing defects on the sub-surface of a section of a helicopter rotor blade

Plate 5 An example of results which may be obtained by applying a different visualisation technique to the same set of data (see Plate 2)

Plate 6 Three dimensional visualisation of impacts on the surface of a composite material inspected using an eddy current system

Plate 7

Multiple sequences from a computer animation procedure used to model the evolution of a disbond on a helicopter rotor blade under stress, using contour plots to delimit the defect area

Original Image (thermograph)

Tune/Posterisation

Edge Enhancement

Contour Lines

Plate 8 Image processing on a thermograph (original image) using posterisation, edge enhancement and contour lines. IP procedures help in determining the boundaries of heat flow and facilitate defect location - in this case impacts on the surface of a composite material

Plate 9

Thermograph of sample 2, side 1, showing impacts of 2.0 J, 2.5 J and 3.0 J

Plate 10 NDT visualisation of the surface of a steel plate using an eddy current system and showing a toe crack and a HAZ crack

Plate 11 Autocad visualisation of a toe crack (red, right-hand side) on a metallic plate (green) with a centreline weld (blue)

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Plate 12 Detection of surface damage by visual inspection of Ecureuil helicopter rotor blade sample 2

Plate 13 Thermograph from infrared inspection of sample 2 (Ecureuil rotor blade). The hot spot shows an area of delamination

Plate 14 Bayesian probability map of the data from Plate 17 showing the degree of support of a defect being present on the area inspected

Plate 15 Results of the eddy current inspection of Ecureuil rotor blade, sample 1

Plate 16

Results of the eddy current inspection of Ecureuil rotor blade, sample 2

Plate 17 Visualisation of eddy current data from the inspection of a section of the BV234 helicopter rotor blade

Plate 18 Detection and visualisation ofimpact damage with eddy current inspection on Sample 1