Safety and Risk Modeling and Its Applications

Springer Series in Reliability Engineering

For further volumes: http://www.springer.com/series/6917

Hoang Pham Editor

Safety and Risk Modeling and Its Applications

123

Prof. Hoang Pham Department of Industrial and Systems Engineering Rutgers University 96 Frelinghuysen Road Piscataway New Jersey 08854-8018 USA e-mail: [email protected]

ISSN 1614-7839 ISBN 978-0-85729-469-2 e-ISBN 978-0-85729-470-8 DOI 10.1007/978-0-85729-470-8 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British library Ó Springer-Verlag London Limited 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: eStudio Calamar, Berlin/Figueres Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Dedicated to Michelle, Hoang Jr. and David

v

Preface

Today’s products and safety critical-systems of most applications have become increasingly complex to build while the demand for safety and cost effective development continues. The interest in safety and risk modeling and assessment has been growing in many years to come. This book, consisting of 14 chapters, aims to present latest methods and techniques for quantifying the safety and risk with emphasis on safety, reliability and risk modeling and their applications in several areas including Aviation Systems and Security, Sensor Detection and Management Decision-Making, and Systems of Systems and Human Integration. The subjects covered include safety engineering, system maintenance, safety in design, failure mode analysis, risk concept and modeling, software safety, human safety, product safety in operating environments, human-hand safety modeling, sensor management, safety decisionmaking, nuclear safety detection, sensor data modeling, uncertainty modeling, aircraft system safety, aviation security and safety, passenger screening models, checked baggage screening models, security inspection, risk-based analysis, economic reliability-safety, quantile risk approach, risk importance measures, probability risk assessment, safety analysis, complex system reliability modeling, risk-informed decision-making, in-service inspection, Markov modeling, hybrid uncertainty system safety assessment, risk prediction, warranty concepts, maintenance policies, and risk management. The book consists of three parts. Part I—Safety and Reliability Modeling— contains four papers, focuses on the modeling and prediction of complex systems and finance management with respect to safety, reliability and maintenance, and economic aspects. The first chapter by Kołowrocki is concerned with the application of limit reliability functions to the reliability evaluation of complex systems. Reliability evaluation of a port grain transportation system and a rope elevator are also discussed. Chapter 2 by Tapiero discusses an economic approach to reliability design and safety based on economic considerations its safety consequences which depend on both system reliability and their safety-consequential effects. Chapter 3 by Chakraborty and Sam deals with the safety evaluation of complex systems. vii

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Preface

The chapter discusses various developments of reliability evaluation of structures with emphasis on the safety evaluation of hybrid uncertain system. An application of reinforced concrete beam is given to illustrate the proposed safety evaluation procedure of hybrid uncertain system. Chapter 4 by Park and Pham discusses on the developments of warranty concepts with various maintenance policies and mathematical methods that used to formulate the mathematical warranty modeling. The concepts of warranty and the overall information about the warranty policy such as warranty’s role, concept, and different types will also be discussed. Part II—Risk Modeling—contains four papers, focuses on the risk modeling, methods, and methodologies. Chapter 5 by Aven provides basic principles and methods in risk analysis. Some key challenges related to the treatment of uncertainties and the incorporation of human and organizational factors are discussed. Chapter 6 by Zio focuses on the use of various classical importance measures such as Birnbaum’s measure, Criticality importance, Fussell-Vesely importance measure in risk-informed applications relates to the ranking, or categorization of components or, more generally, basic events, with respect to their risk-significance. Several applications of risk importance measures are also discussed. Chapter 7 by Wang and Pham discusses recent modeling the reliability of complex degradation systems with competing risks as well as random shocks. Further research related to this subject on the condition-based maintenance and the maintenance policies for multi-component systems embedded within the framework of dependent competing risk of degradation wear and random shocks are also discussed. Chapter 8 by Otsuka discusses the framework of the system design review based on failure modes (DRBFM) based on the design concept and its procedure to identify both the latent problems and misunderstood problems during the system evaluation process. It can be used to help design management team to predict number of failure modes which can improve reliability and safety of products in use. Part III—Applications—contains six papers, aims to discuss various applications in three areas that are related to safety and risk such as: Aviation Systems and Security, Sensor Detection and Management Decision-Making, and System of Systems and Human Integration. In the application of aviation systems and security, Chap. 9 by McLay discusses various analytical modeling approaches including checked baggage screening models and passenger screening models for managing risk in aviation security screening systems based on probabilistic methods. It focuses on passenger screening problems, an important and highly visible aspect of aviation security. Chapter 10 by Oztekin and Luxhoj discusses a generalized hybrid Fuzzy-Bayesian methodology for modeling the risk and uncertainty associated with complex realworld systems and the emergent Unmanned Aircraft Systems (UAS) operations in the National Airspace System (NAS) in particular. The application of sensor detection and management decision-making, Chap. 11 by Carpenter, Cheng, Roberts, and Xie describes a variety of approaches to sensor management problems of nuclear detection. Their approaches in the project include: the methods to exploit data from radiation sensors and shipping

Preface

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manifests for classification and decision-making; ways to optimize sequential decisions in layered inspection processes; detection using a fleet of mobile radiation sensors; and data sampling strategies for nuclear detection. Chapter 12 by Verma, Srividya, Gopika, and Rao focuses on the risk-informed decision-making based on probabilistic safety assessment (PSA) methodology with applications in nuclear power plant safety such as technical specification optimization and risk informed in-service inspection (ISI). The goal of risk informed ISI is to allow the use of risk assessment, understanding of component specific degradation mechanisms, and to establish an effective plant integrity management program, which maintains plant safety. The application of system in systems and human integration, Chap. 13 by Schneidewind describes an application case study that illustrates how software risk and safety reliability analysis can be used to not only reduce the risk of software failure but also to improve the reliability of the entire software product using sequential testing approach. Chapter 14 by Marler et al. focuses on the predictive modeling from a biomechanical perspective between human body performance, hand modeling capabilities and cognitive modeling. A three pronged approach to hand analysis such as model development, reach analysis, and grasping prediction is also discussed. All the chapters are written by more than 25 leading experts in the field with a hope to provide readers the gap between theory and practice and to trigger new research challenges in safety and risk in practices. I am deeply indebted and wish to thank all of them for their contributions and cooperation. Thanks are also due to the Springer staff for their editorial work. I hope that the readers including engineers, teachers, scientists, postgraduates, researchers, managers, and practitioners will find this book as a state-of-thereferences survey and as a valuable resource for understanding the latest developments in both qualitative and quantitative methods of safety and risk analysis and their applications in complex operating environments. Piscataway, New Jersey, August 2010

Hoang Pham

Contents

Part I

Safety and Reliability Modeling

Reliability Modelling of Complex Systems . . . . . . . . . . . . . . . . . . . . . Krzysztof Kołowrocki

3

The Price of Safety and Economic Reliability . . . . . . . . . . . . . . . . . . . Charles S. Tapiero

55

Reliability Analysis of Structures Under Hybrid Uncertainty . . . . . . . Subrata Chakraborty and Palash Chandra Sam

77

Maintenance and Warranty Concepts . . . . . . . . . . . . . . . . . . . . . . . . Minjae Park and Hoang Pham

101

Part II

Risk Modeling

Risk Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Terje Aven

125

Risk Importance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enrico Zio

151

Dependent Competing-Risk Degradation Systems . . . . . . . . . . . . . . . . Yaping Wang and Hoang Pham

197

Risk and Design Management Based on Failure Mode . . . . . . . . . . . . Yuichi Otsuka

219

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Part III

Contents

Applications

Risk-Based Resource Allocation Models for Aviation Security. . . . . . . Laura A. McLay Complex Risk and Uncertainty Modeling for Emergent Aviation Systems: An Application . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmet Oztekin and James T. Luxhøj

243

263

Sensor Management Problems of Nuclear Detection . . . . . . . . . . . . . . Tamra Carpenter, Jerry Cheng, Fred Roberts and Minge Xie

299

Risk-Informed Decision Making in Nuclear Power Plants . . . . . . . . . . A. K. Verma, Ajit Srividya, Vinod Gopika and Karanki Durga Rao

325

Risk, Reliability, Safety, and Testing Case Study . . . . . . . . . . . . . . . . Norman Schneidewind

365

Human Grasp Prediction and Analysis. . . . . . . . . . . . . . . . . . . . . . . . Tim Marler, Ross Johnson, Faisal Goussous, Chris Murphy, Steve Beck and Karim Abdel-Malek

397

About the Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

425

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors

Karim Abdel-Malek, Center for Computer Aided Design, University of Iowa, 111 Engineering Research Facility, Iowa City, Iowa 52242, USA Terje Aven, University of Stavanger, Norway Steve Beck, Center for Computer Aided Design, University of Iowa, 111 Engineering Research Facility, Iowa City, Iowa 52242, USA Tamra Carpenter, DIMACS, Rutgers University, Piscataway, NJ 08854, USA Subrata Chakraborty, Department of Civil Engineering, Bengal Engineering and Science University, Shibpur, Howrah, 711103, India Jerry Cheng, DIMACS, Rutgers University, Piscataway, NJ 08854, USA V. Gopika, Bhabha Atomic Research Centre, Mumbai, India Faisal Goussous, Center for Computer Aided Design, University of Iowa, 111 Engineering Research Facility, Iowa City, Iowa 52242, USA Ross Johnson, Center for Computer Aided Design, University of Iowa, 111 Engineering Research Facility, Iowa City, Iowa 52242, USA Krzysztof Kołowrocki, Maritime University, ul. Morska 81-87, 81-225 Gdynia, Poland James T. Luxhøj, Department of Industrial and Systems Engineering, Rutgers University, 08855-8018, Piscataway, USA Tim Marler, Center for Computer Aided Design, University of Iowa, 111 Engineering Research Facility, Iowa City, Iowa 52242, USA Laura A. McLay, Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, 1015 Floyd Avenue, P.O. Box 843083, Richmond, Virginia 23284, USA. e-mail: [email protected]

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Contributors

Chris Murphy, Center for Computer Aided Design, University of Iowa, 111 Engineering Research Facility, Iowa City, Iowa 52242, USA Yuichi Otsuka, Nagaoka University of Technology, Japan Ahmet Oztekin, Department of Industrial and Systems Engineering, Rutgers University, Piscataway, USA Minjae Park, College of Business Administration, Hongik University, Sangsu-dong, Mapogu, Seoul 121-791, Korea Hoang Pham, Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855-8018, USA K. Durga Rao, Paul Scherrer Institute, Villigen PSI, Switzerland Fred Roberts, DIMACS, Rutgers University, Piscataway, NJ 08854, USA Palash Chandra Sam, Department of Civil Engineering, Bengal Engineering and Science University, Shibpur, Howrah 711103, India Norman Schneidewind, Naval Postgraduate School, 1411 Cunningham Road, Monterey, CA 93943-5219, USA A. Srividya, Indian Institute of Technology Bombay, Mumbai, India Charles S. Tapiero, Department of Finance and Risk Engineering, The Polytechnic Institute of New York University, Brooklyn, New York, USA A. K. Verma, Indian Institute of Technology Bombay, Mumbai, India Yaping Wang, Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855-8018, USA Minge Xie, DIMACS, Rutgers University, Rutgers University, Piscataway, NJ 08854, USA Enrico Zio, Politecnico di Milano, Italy

Part I

Safety and Reliability Modeling

Reliability Modelling of Complex Systems Krzysztof Kołowrocki

1 Introduction Many technical systems belong to the class of complex systems as a result of the large number of components they are built of and their complicated operating processes. As a rule these are series systems composed of large number of components. Sometimes the series systems have either components or subsystems reserved and then they become parallel–series or series–parallel reliability structures. We meet large series systems, for instance, in piping transportation of water, gas, oil and various chemical substances. Large systems of these kinds are also used in electrical energy distribution. A city bus transportation system composed of a number of communication lines each serviced by one bus may be a model series system, if we treat it as not failed, when all its lines are able to transport passengers. If the communication lines have at their disposal several buses we may consider it as either a parallel–series system or an ‘‘m out of n’’ system. The simplest example of a parallel system or an ‘‘m out of n’’ system may be an electrical cable composed of a number of wires, which are its basic components, whereas the transmitting electrical network may be either a parallel–series system or an ‘‘m out of n’’-series system. Large systems of these types are also used in telecommunication, in rope transportation and in transport using belt conveyers and elevators. Rope transportation systems like port elevators and ship-rope elevators used in shipyards during ship docking are model examples of series–parallel and parallel–series systems. In the case of large systems, the determination of the exact reliability functions of the systems leads us to complicated formulae that are often useless for reliability practitioners. One of the important techniques in this situation is the

K. Kołowrocki (&) Maritime University, ul. Morska 81-87, 81-225 Gdynia, Poland e-mail: [email protected] H. Pham (ed.), Safety and Risk Modeling and Its Applications, Springer Series in Reliability Engineering, DOI: 10.1007/978-0-85729-470-8_1, Ó Springer-Verlag London Limited 2011

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K. Kołowrocki

asymptotic approach to system reliability evaluation. In this approach, instead of the preliminary complex formula for the system reliability function, after assuming that the number of system components tends to infinity and finding the limit reliability of the system, we obtain its simplified form. The mathematical methods used in the asymptotic approach to the system reliability analysis of large systems are based on limit theorems on order statistics distributions, considered in very wide literature, for instance in [4, 5, 8, 23]. These theorems have generated the investigation concerned with limit reliability functions of the systems composed of two-state components. The main and fundamental results on this subject that determine the three-element classes of limit reliability functions for homogeneous series systems and for homogeneous parallel systems have been established by Gniedenko in [6]. These results are also presented, sometimes with different proofs, for instance in subsequent works [1, 9]. The generalisations of these results for homogeneous ‘‘m out of n’’ systems have been formulated and proved by Smirnow in [19], where the sevenelement class of possible limit reliability functions for these systems has been fixed. As it has been done for homogeneous series and parallel systems classes of limit reliability functions have been fixed by Chernoff and Teicher in [2] for homogeneous series–parallel and parallel–series systems. Their results were concerned with so-called ‘‘quadratic’’ systems only. They have fixed limit reliability functions for the homogeneous series–parallel systems with the number of series subsystems equal to the number of components in these subsystems, and for the homogeneous parallel–series systems with the number of parallel subsystems equal to the number of components in these subsystems. Kołowrocki has generalised their results for non-‘‘quadratic’’ and non-homogeneous series–parallel and parallel–series systems in [9]. All these results may also be found, for instance, in [10]. The results concerned with the asymptotic approach to system reliability analysis have become the basis for the investigation concerned with domains of attraction [10, 14] for the limit reliability functions of the considered systems and the investigation concerned with the reliability of large hierarchical systems as well [3, 10]. Domains of attraction for limit reliability functions of two-state systems are introduced. They are understood as the conditions that the reliability functions of the particular components of the system have to satisfy in order that the system limit reliability function is one of the limit reliability functions from the previously fixed class for this system. Exemplary theorems concerned with domains of attraction for limit reliability functions of homogeneous series systems are presented here and the application of one of them is illustrated. Hierarchical series–parallel and parallel–series systems of any order are defined, their reliability functions are determined and limit theorems on their reliability functions are applied to reliability evaluation of exemplary hierarchical systems of order two. All the results so far described have been obtained under the linear normalisation of the system lifetimes. The chapter contains the results described above and comments on their newest generalisations recently presented in [10].

Reliability Modelling of Complex Systems

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The chapter is concerned with the application of limit reliability functions to the reliability evaluation of large systems. Two-state large non-repaired systems composed of independent components are considered. The asymptotic approach to the system reliability investigation and the system limit reliability function is defined. Two-state homogeneous series, parallel and series–parallel systems are defined and their exact reliability functions are determined. The classes of limit reliability functions of these systems are presented. The results of the investigation concerned with domains of attraction for the limit reliability functions of the considered systems and the investigation concerned with the reliability of large hierarchical systems as well are discussed in the paper. The chapter contains exemplary applications of the presented facts to the reliability evaluation of large technical systems. Moreover, series-‘‘m out of n’’ systems and ‘‘m out of n’’-series systems are defined, and exemplary theorems on their limit reliability functions are presented and applied to the reliability evaluation of a piping transportation system and a rope elevator. Applications of the asymptotic approach in large series systems reliability improvement are also presented. The paper is completed by showing the possibility of applying the asymptotic approach to the reliability analysis of large systems placed in their variable operation processes. In this scope, the asymptotic approach to reliability evaluation for a port grain transportation system related to its operation process is performed.

2 Reliability of Two-State Systems We assume that Ei ;

i ¼ 1; 2; . . .; n;

n 2 N;

are two-state components of the system having reliability functions Ri ðtÞ ¼ PðTi [ tÞ;

t 2 ð1; 1Þ;

where Ti ;

i ¼ 1; 2; . . .; n;

are independent random variables representing the lifetimes of components Ei with distribution functions Fi ðtÞ ¼ PðTi  tÞ;

t 2 ð1; 1Þ;

The simplest two-state reliability structures are series and parallel systems. We define these systems first. Definition 1 We call a two-state system series if its lifetime T is given by T ¼ min fTi g: 1in

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K. Kołowrocki

Fig. 1 The scheme of a series system

E2

E1

En

Fig. 2 The scheme of a parallel system

E1

E2

. . .

En

The scheme of a series system is given in Fig. 1. Definition 1 means that the series system is not failed if and only if all its components are not failed and therefore its reliability function is given by R n ðt Þ ¼

n Y

Ri ðtÞ; t 2 ð1; 1Þ:

ð1Þ

i¼1

Definition 2 We call a two-state system parallel if its lifetime T is given by T ¼ max fTi g: 1in

The scheme of a parallel system is given in Fig. 2. Definition 2 means that the parallel system is failed if and only if all its components are failed and therefore its reliability function is given by Rn ðtÞ ¼ 1 

n Y

Fi ðtÞ;

t 2 ð1; 1Þ:

ð2Þ

i¼1

Another basic, a bit more complex, two-state reliability structure is a series– parallel system. To define it, we assume that Eij ;

i ¼ 1; 2; . . .; kn ;

j ¼ 1; 2; . . .; li ; kn ;

l1 ; l2 ; . . .; lkn 2 N;

Reliability Modelling of Complex Systems Fig. 3 The scheme of a series–parallel system

7 E11

E1 2

. . .

E 21

E2 2

. . .

. . .

. . .

. . .

. . .

Ek 1 n

. . .

. . .

. . .

E1 l1

E 2l

2

. . .

. . .

Ek n 2

. . .

. . .

. . .

. . .

. . .

. . .

. . . Ek l n kn

are two-state components of the system having reliability functions   Rij ðtÞ ¼ P Tij [ t ; t 2 ð1; 1Þ; where Tij ;

i ¼ 1; 2; . . .; kn ;

j ¼ 1; 2; . . .; li ;

are independent random variables representing the lifetimes of components Eij with distribution functions   Fij ðtÞ ¼ P Tij  t ; t 2 ð1; 1Þ: Definition 3 We call a two-state system series–parallel if its lifetime T is given by T ¼ max f min fTij gg: 1  i  kn 1  j  li

The scheme of a regular series–parallel system is given in Fig. 3. By joining formulae (1) and (2) for the reliability functions of two-state series and parallel systems it is easy to conclude that the reliability function of the two-state series–parallel system is given by " # kn li Y Y Rkn ;l1 ;l2 ;...;lkn ðtÞ ¼ 1  1 Rij ðtÞ ; t 2 ð1; 1Þ; ð3Þ i¼1

j¼1

where kn is the number of series subsystems linked in parallel and li are the numbers of components in the series subsystems. Definition 4 We call a two-state series–parallel system regular if l1 ¼ l2 ¼    ¼ lkn ¼ ln ;

ln 2 N;

i.e., if the numbers of components in its series subsystems are equal. The scheme of a regular series–parallel system is given in Fig. 4. Definition 5 We call a two-state system homogeneous if its component lifetimes have an identical distribution function FðtÞ; i.e., if its components have the same reliability function

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K. Kołowrocki

Fig. 4 The scheme of a regular series–parallel system

E11

E1 2

. . .

E1 ln

E 21

E2 2

. . .

E2ln

. . .

. . .

. . .

. . .

Ekn 1

RðtÞ ¼ 1  F ðtÞ;

. . .

. . .

. . .

Ekn 2

. . .

. . .

. . .

. . .

. . .

. . .

E k n ln

t 2 ð1; 1Þ:

The above definition and equations (1–3) result in the simplified formulae for the reliability functions of the homogeneous systems stated in the following corollary. Corollary 1 The reliability function of the homogeneous two-state system is given by – for a series system Rn ðtÞ ¼ ½RðtÞn ;

t 2 ð1; 1Þ;

ð4Þ

– for a parallel system Rn ðtÞ ¼ 1  ½F ðtÞn ;

t 2 ð1; 1Þ;

– for a regular series–parallel system h ikn Rkn ;ln ðtÞ ¼ 1  1  ½RðtÞln ;

t 2 ð1; 1Þ:

ð5Þ

ð6Þ

3 Asymptotic Approach to System Reliability The asymptotic approach to the reliability of two-state systems depends on the investigation of limit distributions of a standardised random variable ðT  bn Þ=an ; where T is the lifetime of a system and an [ 0 and bn 2 ð1; 1Þ are suitably chosen numbers called normalising constants. Since PððT  bn Þ=an [ tÞ ¼ PðT [ an t þ bn Þ ¼ Rn ðan t þ bn Þ; where Rn(t) is a reliability function of a system composed of n components, then the following definition becomes natural.

Reliability Modelling of Complex Systems

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Definition 6 We call a reliability function