Ilia B. Frenkel, Alex Karagrigoriou, Anatoly Lisnianski, Andre V. Kleyner
Applied Reliability Engineering and Risk Analysis
Probabilistic Models and Statistical Inference
Ilia B. Frenkel, Alex Karagrigoriou, Anatoly Lisnianski, Andre V. Kleyner
Applied Reliability Engineering and Risk Analysis
Probabilistic Models and Statistical Inference
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The first book to treat the important areas of degradation analysis, multi-state system reliability, networks, and large scale systems in one comprehensive volume, Applied Reliability Engineering and Risk Analysis presents a complete resource on the important developments in applied reliability engineering, probabilistic models, and risk analysis. Consolidating the latest research activities in several areas of reliability science, the text provides scientists, engineers, researchers, and students with all the recent advances, most up-to-date research, and results in the individual fields.…mehr
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The first book to treat the important areas of degradation analysis, multi-state system reliability, networks, and large scale systems in one comprehensive volume, Applied Reliability Engineering and Risk Analysis presents a complete resource on the important developments in applied reliability engineering, probabilistic models, and risk analysis. Consolidating the latest research activities in several areas of reliability science, the text provides scientists, engineers, researchers, and students with all the recent advances, most up-to-date research, and results in the individual fields. Chapters are written by leaders in their respective fields.
This complete resource on the theory and applications of reliability engineering, probabilistic models and risk analysis consolidates all the latest research, presenting the most up-to-date developments in this field.
With comprehensive coverage of the theoretical and practical issues of both classic and modern topics, it also provides a unique commemoration to the centennial of the birth of Boris Gnedenko, one of the most prominent reliability scientists of the twentieth century.
Key features include:
expert treatment of probabilistic models and statistical inference from leading scientists, researchers and practitioners in their respective reliability fields
detailed coverage of multi-state system reliability, maintenance models, statistical inference in reliability, systemability, physics of failures and reliability demonstration
many examples and engineering case studies to illustrate the theoretical results and their practical applications in industry
Applied Reliability Engineering and Risk Analysis is one of the first works to treat the important areas of degradation analysis, multi-state system reliability, networks and large-scale systems in one comprehensive volume. It is an essential reference for engineers and scientists involved in reliability analysis, applied probability and statistics, reliability engineering and maintenance, logistics, and quality control. It is also a useful resource for graduate students specialising in reliability analysis and applied probability and statistics.
Dedicated to the Centennial of the birth of Boris Gnedenko, renowned Russian mathematician and reliability theorist
This complete resource on the theory and applications of reliability engineering, probabilistic models and risk analysis consolidates all the latest research, presenting the most up-to-date developments in this field.
With comprehensive coverage of the theoretical and practical issues of both classic and modern topics, it also provides a unique commemoration to the centennial of the birth of Boris Gnedenko, one of the most prominent reliability scientists of the twentieth century.
Key features include:
expert treatment of probabilistic models and statistical inference from leading scientists, researchers and practitioners in their respective reliability fields
detailed coverage of multi-state system reliability, maintenance models, statistical inference in reliability, systemability, physics of failures and reliability demonstration
many examples and engineering case studies to illustrate the theoretical results and their practical applications in industry
Applied Reliability Engineering and Risk Analysis is one of the first works to treat the important areas of degradation analysis, multi-state system reliability, networks and large-scale systems in one comprehensive volume. It is an essential reference for engineers and scientists involved in reliability analysis, applied probability and statistics, reliability engineering and maintenance, logistics, and quality control. It is also a useful resource for graduate students specialising in reliability analysis and applied probability and statistics.
Dedicated to the Centennial of the birth of Boris Gnedenko, renowned Russian mathematician and reliability theorist
Produktdetails
- Produktdetails
- Wiley Series in Quality and Reliability Engineering
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 448
- Erscheinungstermin: 11. November 2013
- Englisch
- Abmessung: 251mm x 174mm x 27mm
- Gewicht: 860g
- ISBN-13: 9781118539422
- ISBN-10: 1118539427
- Artikelnr.: 38474800
- Wiley Series in Quality and Reliability Engineering
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 448
- Erscheinungstermin: 11. November 2013
- Englisch
- Abmessung: 251mm x 174mm x 27mm
- Gewicht: 860g
- ISBN-13: 9781118539422
- ISBN-10: 1118539427
- Artikelnr.: 38474800
Ilia Frenkel, Center for Reliability and Risk Management, Industrial Engineering and Management Department, SCE - Shamoon College of Engineering, Israel Ilia has forty years academic experience, teaching in Russia and Israel. Currently he is a senior lecturer and Director of the Centre for Reliability and Risk Management in the Industrial Engineering and Management Department of the SCE - Shamoon College of Engineering, Israel. Previously he worked as Department Chair and Associate Professor in the Applied Mathematics and Computers Department at Volgograd Civil Engineering Institute. He is a member of the editorial board on Maintenance and Reliability, Communications in Dependability and Quality Management, and has published scientific articles and book chapters in the fields of reliability, applied statistics and production and operation management. Alex Karagrigoriou, Department of Mathematics and Statistics, University of Cyprus Alex is Associate Professor of Statistics, Department of Mathematics and Statistics, University of Cyprus and Professor of Probability and Statistics, University of the Aegean. He worked at the University of Maryland, the United States Department of Agriculture and the Institute of Statistical Sciences, Taiwan, and taught thirty-two courses at the Universities of Maryland, Athens, the Aegean, and Cyprus. He has been involved in the organization of eight international conferences. He has written two textbooks on statistical analysis, teaching notes for undergraduate and graduate courses, and has published more than fifty articles on statistics and applied probability. Alex has served as reviewer for the United States National Security Council and the United Kingdom Economic and Social Research Council. Anatoly Lisnianski, Reliability Department, The Israel Electric Corporation Ltd., Israel Anatoly is an engineering expert in the Reliability Department of The Israel Electric Corporation Ltd., Israel, an adjunct senior lecturer in Haifa University, Israel, and Scientific Supervisor of the Centre for Reliability and Risk Management in the Industrial Engineering and Management Department of the SCE - Shamoon College of Engineering, Israel. Previous to this he was Senior Researcher in Federal Scientific & Production Center "Aurora" in St-Petersburg, Russia. He is a Senior Member of IEEE, Member of Israel Society of Quality and Israel Statistical Association, and is an author of more than one hundred publications in the field of reliability and applied probability. He has been guest editor for International Journal of Reliability, Quality and Safety Engineering. Andre Kleyner, Global Reliability Engineering Leader, Delphi Electronics and Safety, USA Andre has twenty-five years of engineering, research, consulting, and managerial experience specializing in the reliability of electronic and mechanical systems. He is currently a Global Reliability Engineering Leader with Delphi Electronics & Safety and an adjunct professor at Purdue University.? He is a senior member of American Society for Quality, a Certified Reliability Engineer, Certified Quality Engineer, and a Six Sigma Black Belt.? He also holds several US and foreign patents and authored multiple publications on the topics of reliability, statistics, warranty management, and lifecycle cost analysis.? Andre Kleyner is Editor of the Wiley Series in Quality & Reliability Engineering.
Remembering Boris Gnedenko xvii List of Contributors xxv Preface xxix
Acknowledgements xxxv Part I DEGRADATION ANALYSIS, MULTI-STATE AND
CONTINUOUS-STATE SYSTEM RELIABILITY 1 Methods of Solutions of Inhomogeneous
Continuous Time Markov Chains for Degradation Process Modeling 3 Yan-Fu Li,
Enrico Zio and Yan-Hui Lin 1.1 Introduction 3 1.2 Formalism of ICTMC 4 1.3
Numerical Solution Techniques 5 1.4 Examples 10 1.5 Comparisons of the
Methods and Guidelines of Utilization 13 1.6 Conclusion 15 References 15 2
Multistate Degradation and Condition Monitoring for Devices with Multiple
Independent Failure Modes 17 Ramin Moghaddass and Ming J. Zuo 2.1
Introduction 17 2.2 Multistate Degradation and Multiple Independent Failure
Modes 19 2.3 Parameter Estimation 23 2.4 Important Reliability Measures of
a Condition-Monitored Device 25 2.5 Numerical Example 27 2.6 Conclusion 28
Acknowledgements 30 References 30 3 Time Series Regression with Exponential
Errors for Accelerated Testing and Degradation Tracking 32 Nozer D.
Singpurwalla 3.1 Introduction 32 3.2 Preliminaries: Statement of the
Problem 33 3.3 Estimation and Prediction by Least Squares 34 3.4 Estimation
and Prediction by MLE 35 3.5 The Bayesian Approach: The Predictive
Distribution 37 Acknowledgements 42 References 42 4 Inverse Lz-Transform
for a Discrete-State Continuous-Time Markov Process and Its Application to
Multi-State System Reliability Analysis 43 Anatoly Lisnianski and Yi Ding
4.1 Introduction 43 4.2 Inverse Lz-Transform: Definitions and Computational
Procedure 44 4.3 Application of Inverse Lz-Transform to MSS Reliability
Analysis 50 4.4 Numerical Example 52 4.5 Conclusion 57 References 58 5
OntheLz-Transform Application for Availability Assessment of an Aging
Multi-State Water Cooling System for Medical Equipment 59 Ilia Frenkel,
Anatoly Lisnianski and Lev Khvatskin 5.1 Introduction 59 5.2 Brief
Description of the Lz-Transform Method 61 5.3 Multi-state Model of the
Water Cooling System for the MRI Equipment 62 5.4 Availability Calculation
75 5.5 Conclusion 76 Acknowledgments 76 References 77 6 Combined Clustering
and Lz-Transform Technique to Reduce the Computational Complexity of a
Multi-State System Reliability Evaluation 78 Yi Ding 6.1 Introduction 78
6.2 The Lz-Transform for Dynamic Reliability Evaluation for MSS 79 6.3
Clustering Composition Operator in the Lz-Transform 81 6.4 Computational
Procedures 83 6.5 Numerical Example 83 6.6 Conclusion 85 References 85 7
Sliding Window Systems with Gaps 87 Gregory Levitin 7.1 Introduction 87 7.2
The Models 89 7.3 Reliability Evaluation Technique 91 7.4 Conclusion 96
References 96 8 Development of Reliability Measures Motivated by Fuzzy Sets
for Systems with Multi- or Infinite-States 98 Zhaojun (Steven) Li and
Kailash C. Kapur 8.1 Introduction 98 8.2 Models for Components and Systems
Using Fuzzy Sets 100 8.3 Fuzzy Reliability for Systems with Continuous or
Infinite States 103 8.4 Dynamic Fuzzy Reliability 104 8.5 System Fuzzy
Reliability 110 8.6 Examples and Applications 111 8.7 Conclusion 117
References 118 9 Imperatives for Performability Design in the Twenty-First
Century 119 Krishna B. Misra 9.1 Introduction 119 9.2 Strategies for
Sustainable Development 120 9.3 Reappraisal of the Performance of Products
and Systems 124 9.4 Dependability and Environmental Risk are Interdependent
126 9.5 Performability: An Appropriate Measure of Performance 126 9.6
Towards Dependable and Sustainable Designs 129 9.7 Conclusion 130
References 130 Part II NETWORKS AND LARGE-SCALE SYSTEMS 10 Network
Reliability Calculations Based on Structural Invariants 135 Ilya B.
Gertsbakh and Yoseph Shpungin 10.1 First Invariant: D-Spectrum, Signature
135 10.2 Second Invariant: Importance Spectrum. Birnbaum Importance Measure
(BIM) 139 10.3 Example: Reliability of a Road Network 141 10.4 Third
Invariant: Border States 142 10.5 Monte Carlo to Approximate the Invariants
144 10.6 Conclusion 146 References 146 11 Performance and Availability
Evaluation of IMS-Based Core Networks 148 Kishor S. Trivedi, Fabio
Postiglione and Xiaoyan Yin 11.1 Introduction 148 11.2 IMS-Based Core
Network Description 149 11.3 Analytic Models for Independent Software
Recovery 151 11.4 Analytic Models for Recovery with Dependencies 155 11.5
Redundancy Optimization 158 11.6 Numerical Results 159 11.7 Conclusion 165
References 165 12 Reliability and Probability of First Occurred Failure for
Discrete-Time Semi-Markov Systems 167 Stylianos Georgiadis, Nikolaos
Limnios and Irene Votsi 12.1 Introduction 167 12.2 Discrete-Time
Semi-Markov Model 168 12.3 Reliability and Probability of First Occurred
Failure 170 12.4 Nonparametric Estimation of Reliability Measures 172 12.5
Numerical Application 176 12.6 Conclusion 178 References 179 13
Single-Source Epidemic Process in a System of Two Interconnected Networks
180 Ilya B. Gertsbakh and Yoseph Shpungin 13.1 Introduction 180 13.2
Failure Process and the Distribution of the Number of Failed Nodes 181 13.3
Network Failure Probabilities 184 13.4 Example 185 13.5 Conclusion 187 13.A
Appendix D: Spectrum (Signature) 188 References 189 Part III MAINTENANCE
MODELS 14 Comparisons of Periodic and Random Replacement Policies 193
Xufeng Zhao and Toshio Nakagawa 14.1 Introduction 193 14.2 Four Policies
195 14.3 Comparisons of Optimal Policies 197 14.4 Numerical Examples 1 199
14.5 Comparisons of Policies with Different Replacement Costs 201 14.6
Numerical Examples 2 202 14.7 Conclusion 203 Acknowledgements 204
References 204 15 Random Evolution of Degradation and Occurrences of Words
in Random Sequences of Letters 205 Emilio De Santis and Fabio Spizzichino
15.1 Introduction 205 15.2 Waiting Times to Words' Occurrences 206 15.3
Some Reliability-Maintenance Models 209 15.4 Waiting Times to Occurrences
of Words and Stochastic Comparisons for Degradation 213 15.5 Conclusions
216 Acknowledgements 217 References 217 16 Occupancy Times for Markov and
Semi-Markov Models in Systems Reliability 218 Alan G. Hawkes, Lirong Cui
and Shijia Du 16.1 Introduction 218 16.2 Markov Models for Systems
Reliability 220 16.3 Semi-Markov Models 222 16.4 Time Interval Omission 225
16.5 Numerical Examples 226 16.6 Conclusion 229 Acknowledgements 229
References 229 17 A Practice of Imperfect Maintenance Model Selection for
Diesel Engines 231 Yu Liu, Hong-Zhong Huang, Shun-Peng Zhu and Yan-Feng Li
17.1 Introduction 231 17.2 Review of Imperfect Maintenance Model Selection
Method 233 17.3 Application to Preventive Maintenance Scheduling of Diesel
Engines 236 17.4 Conclusion 244 Acknowledgment 245 References 245 18
Reliability of Warm Standby Systems with Imperfect Fault Coverage 246 Rui
Peng, Ola Tannous, Liudong Xing and Min Xie 18.1 Introduction 246 18.2
Literature Review 247 18.3 The BDD-Based Approach 250 18.4 Conclusion 253
Acknowledgments 254 References 254 Part IV STATISTICAL INFERENCE IN
RELIABILITY 19 On the Validity of the Weibull-Gnedenko Model 259 Vilijandas
Bagdonavi¡cius, Mikhail Nikulin and Ruta Levuliene 19.1 Introduction 259
19.2 Integrated Likelihood Ratio Test 261 19.3 Tests based on the
Difference of Non-Parametric and Parametric Estimators of the Cumulative
Distribution Function 264 19.4 Tests based on Spacings 266 19.5 Chi-Squared
Tests 267 19.6 Correlation Test 269 19.7 Power Comparison 269 19.8
Conclusion 272 References 272 20 Statistical Inference for Heavy-Tailed
Distributions in Reliability Systems 273 Ilia Vonta and Alex Karagrigoriou
20.1 Introduction 273 20.2 Heavy-Tailed Distributions 274 20.3 Examples of
Heavy-Tailed Distributions 277 20.4 Divergence Measures 280 20.5 Hypothesis
Testing 284 20.6 Simulations 286 20.7 Conclusion 287 References 287 21
Robust Inference based on Divergences in Reliability Systems 290 Abhik
Ghosh, Avijit Maji and Ayanendranath Basu 21.1 Introduction 290 21.2 The
Power Divergence (PD) Family 291 21.3 Density Power Divergence (DPD) and
Parametric Inference 296 21.4 A Generalized Form: The S-Divergence 301 21.5
Applications 304 21.6 Conclusion 306 References 306 22 COM-Poisson Cure
Rate Models and Associated Likelihood-based Inference with Exponential and
Weibull Lifetimes 308 N. Balakrishnan and Suvra Pal 22.1 Introduction 308
22.2 Role of Cure Rate Models in Reliability 310 22.3 The COM-Poisson Cure
Rate Model 310 22.4 Data and the Likelihood 311 22.5 EM Algorithm 312 22.6
Standard Errors and Asymptotic Confidence Intervals 314 22.7 Exponential
Lifetime Distribution 314 22.8 Weibull Lifetime Distribution 322 22.9
Analysis of Cutaneous Melanoma Data 334 22.10 Conclusion 337 22.A1 Appendix
A1: E-Step and M-Step Formulas for Exponential Lifetimes 337 22.A2 Appendix
A2: E-Step and M-Step Formulas for Weibull Lifetimes 341 22.B1 Appendix B1:
Observed Information Matrix for Exponential Lifetimes 344 22.B2 Appendix
B2: Observed Information Matrix for Weibull Lifetimes 346 References 347 23
Exponential Expansions for Perturbed Discrete Time Renewal Equations 349
Dmitrii Silvestrov and Mikael Petersson 23.1 Introduction 349 23.2
Asymptotic Results 350 23.3 Proofs 353 23.4 Discrete Time Regenerative
Processes 358 23.5 Queuing and Risk Applications 359 References 361 24 On
Generalized Extreme Shock Models under Renewal Shock Processes 363 Ji Hwan
Cha and Maxim Finkelstein 24.1 Introduction 363 24.2 Generalized Extreme
Shock Models 364 24.3 Specific Models 367 24.4 Conclusion 373
Acknowledgements 373 References 373 Part V SYSTEMABILITY,
PHYSICS-OF-FAILURE AND RELIABILITY DEMONSTRATION 25 Systemability Theory
and its Applications 377 Hoang Pham 25.1 Introduction 377 25.2
Systemability Measures 378 25.3 Systemability Analysis of k-out-of-n
Systems 379 25.4 Systemability Function Approximation 380 25.5
Systemability with Loglog Distribution 383 25.6 Sensitivity Analysis 384
25.7 Applications: Red Light Camera Systems 385 25.8 Conclusion 387
References 387 26 Physics-of-Failure based Reliability Engineering 389
Pedro O. Quintero and Michael Pecht 26.1 Introduction 389 26.2
Physics-of-Failure-based Reliability Assessment 393 26.3 Uses of
Physics-of-Failure 398 26.4 Conclusion 400 References 400 27 Accelerated
Testing: Effect of Variance in Field Environmental Conditions on the
Demonstrated Reliability 403 Andre Kleyner 27.1 Introduction 403 27.2
Accelerated Testing and Field Stress Variation 404 27.3 Case Study:
Reliability Demonstration Using Temperature Cycling Test 405 27.4
Conclusion 408 References 408 Index 409
Acknowledgements xxxv Part I DEGRADATION ANALYSIS, MULTI-STATE AND
CONTINUOUS-STATE SYSTEM RELIABILITY 1 Methods of Solutions of Inhomogeneous
Continuous Time Markov Chains for Degradation Process Modeling 3 Yan-Fu Li,
Enrico Zio and Yan-Hui Lin 1.1 Introduction 3 1.2 Formalism of ICTMC 4 1.3
Numerical Solution Techniques 5 1.4 Examples 10 1.5 Comparisons of the
Methods and Guidelines of Utilization 13 1.6 Conclusion 15 References 15 2
Multistate Degradation and Condition Monitoring for Devices with Multiple
Independent Failure Modes 17 Ramin Moghaddass and Ming J. Zuo 2.1
Introduction 17 2.2 Multistate Degradation and Multiple Independent Failure
Modes 19 2.3 Parameter Estimation 23 2.4 Important Reliability Measures of
a Condition-Monitored Device 25 2.5 Numerical Example 27 2.6 Conclusion 28
Acknowledgements 30 References 30 3 Time Series Regression with Exponential
Errors for Accelerated Testing and Degradation Tracking 32 Nozer D.
Singpurwalla 3.1 Introduction 32 3.2 Preliminaries: Statement of the
Problem 33 3.3 Estimation and Prediction by Least Squares 34 3.4 Estimation
and Prediction by MLE 35 3.5 The Bayesian Approach: The Predictive
Distribution 37 Acknowledgements 42 References 42 4 Inverse Lz-Transform
for a Discrete-State Continuous-Time Markov Process and Its Application to
Multi-State System Reliability Analysis 43 Anatoly Lisnianski and Yi Ding
4.1 Introduction 43 4.2 Inverse Lz-Transform: Definitions and Computational
Procedure 44 4.3 Application of Inverse Lz-Transform to MSS Reliability
Analysis 50 4.4 Numerical Example 52 4.5 Conclusion 57 References 58 5
OntheLz-Transform Application for Availability Assessment of an Aging
Multi-State Water Cooling System for Medical Equipment 59 Ilia Frenkel,
Anatoly Lisnianski and Lev Khvatskin 5.1 Introduction 59 5.2 Brief
Description of the Lz-Transform Method 61 5.3 Multi-state Model of the
Water Cooling System for the MRI Equipment 62 5.4 Availability Calculation
75 5.5 Conclusion 76 Acknowledgments 76 References 77 6 Combined Clustering
and Lz-Transform Technique to Reduce the Computational Complexity of a
Multi-State System Reliability Evaluation 78 Yi Ding 6.1 Introduction 78
6.2 The Lz-Transform for Dynamic Reliability Evaluation for MSS 79 6.3
Clustering Composition Operator in the Lz-Transform 81 6.4 Computational
Procedures 83 6.5 Numerical Example 83 6.6 Conclusion 85 References 85 7
Sliding Window Systems with Gaps 87 Gregory Levitin 7.1 Introduction 87 7.2
The Models 89 7.3 Reliability Evaluation Technique 91 7.4 Conclusion 96
References 96 8 Development of Reliability Measures Motivated by Fuzzy Sets
for Systems with Multi- or Infinite-States 98 Zhaojun (Steven) Li and
Kailash C. Kapur 8.1 Introduction 98 8.2 Models for Components and Systems
Using Fuzzy Sets 100 8.3 Fuzzy Reliability for Systems with Continuous or
Infinite States 103 8.4 Dynamic Fuzzy Reliability 104 8.5 System Fuzzy
Reliability 110 8.6 Examples and Applications 111 8.7 Conclusion 117
References 118 9 Imperatives for Performability Design in the Twenty-First
Century 119 Krishna B. Misra 9.1 Introduction 119 9.2 Strategies for
Sustainable Development 120 9.3 Reappraisal of the Performance of Products
and Systems 124 9.4 Dependability and Environmental Risk are Interdependent
126 9.5 Performability: An Appropriate Measure of Performance 126 9.6
Towards Dependable and Sustainable Designs 129 9.7 Conclusion 130
References 130 Part II NETWORKS AND LARGE-SCALE SYSTEMS 10 Network
Reliability Calculations Based on Structural Invariants 135 Ilya B.
Gertsbakh and Yoseph Shpungin 10.1 First Invariant: D-Spectrum, Signature
135 10.2 Second Invariant: Importance Spectrum. Birnbaum Importance Measure
(BIM) 139 10.3 Example: Reliability of a Road Network 141 10.4 Third
Invariant: Border States 142 10.5 Monte Carlo to Approximate the Invariants
144 10.6 Conclusion 146 References 146 11 Performance and Availability
Evaluation of IMS-Based Core Networks 148 Kishor S. Trivedi, Fabio
Postiglione and Xiaoyan Yin 11.1 Introduction 148 11.2 IMS-Based Core
Network Description 149 11.3 Analytic Models for Independent Software
Recovery 151 11.4 Analytic Models for Recovery with Dependencies 155 11.5
Redundancy Optimization 158 11.6 Numerical Results 159 11.7 Conclusion 165
References 165 12 Reliability and Probability of First Occurred Failure for
Discrete-Time Semi-Markov Systems 167 Stylianos Georgiadis, Nikolaos
Limnios and Irene Votsi 12.1 Introduction 167 12.2 Discrete-Time
Semi-Markov Model 168 12.3 Reliability and Probability of First Occurred
Failure 170 12.4 Nonparametric Estimation of Reliability Measures 172 12.5
Numerical Application 176 12.6 Conclusion 178 References 179 13
Single-Source Epidemic Process in a System of Two Interconnected Networks
180 Ilya B. Gertsbakh and Yoseph Shpungin 13.1 Introduction 180 13.2
Failure Process and the Distribution of the Number of Failed Nodes 181 13.3
Network Failure Probabilities 184 13.4 Example 185 13.5 Conclusion 187 13.A
Appendix D: Spectrum (Signature) 188 References 189 Part III MAINTENANCE
MODELS 14 Comparisons of Periodic and Random Replacement Policies 193
Xufeng Zhao and Toshio Nakagawa 14.1 Introduction 193 14.2 Four Policies
195 14.3 Comparisons of Optimal Policies 197 14.4 Numerical Examples 1 199
14.5 Comparisons of Policies with Different Replacement Costs 201 14.6
Numerical Examples 2 202 14.7 Conclusion 203 Acknowledgements 204
References 204 15 Random Evolution of Degradation and Occurrences of Words
in Random Sequences of Letters 205 Emilio De Santis and Fabio Spizzichino
15.1 Introduction 205 15.2 Waiting Times to Words' Occurrences 206 15.3
Some Reliability-Maintenance Models 209 15.4 Waiting Times to Occurrences
of Words and Stochastic Comparisons for Degradation 213 15.5 Conclusions
216 Acknowledgements 217 References 217 16 Occupancy Times for Markov and
Semi-Markov Models in Systems Reliability 218 Alan G. Hawkes, Lirong Cui
and Shijia Du 16.1 Introduction 218 16.2 Markov Models for Systems
Reliability 220 16.3 Semi-Markov Models 222 16.4 Time Interval Omission 225
16.5 Numerical Examples 226 16.6 Conclusion 229 Acknowledgements 229
References 229 17 A Practice of Imperfect Maintenance Model Selection for
Diesel Engines 231 Yu Liu, Hong-Zhong Huang, Shun-Peng Zhu and Yan-Feng Li
17.1 Introduction 231 17.2 Review of Imperfect Maintenance Model Selection
Method 233 17.3 Application to Preventive Maintenance Scheduling of Diesel
Engines 236 17.4 Conclusion 244 Acknowledgment 245 References 245 18
Reliability of Warm Standby Systems with Imperfect Fault Coverage 246 Rui
Peng, Ola Tannous, Liudong Xing and Min Xie 18.1 Introduction 246 18.2
Literature Review 247 18.3 The BDD-Based Approach 250 18.4 Conclusion 253
Acknowledgments 254 References 254 Part IV STATISTICAL INFERENCE IN
RELIABILITY 19 On the Validity of the Weibull-Gnedenko Model 259 Vilijandas
Bagdonavi¡cius, Mikhail Nikulin and Ruta Levuliene 19.1 Introduction 259
19.2 Integrated Likelihood Ratio Test 261 19.3 Tests based on the
Difference of Non-Parametric and Parametric Estimators of the Cumulative
Distribution Function 264 19.4 Tests based on Spacings 266 19.5 Chi-Squared
Tests 267 19.6 Correlation Test 269 19.7 Power Comparison 269 19.8
Conclusion 272 References 272 20 Statistical Inference for Heavy-Tailed
Distributions in Reliability Systems 273 Ilia Vonta and Alex Karagrigoriou
20.1 Introduction 273 20.2 Heavy-Tailed Distributions 274 20.3 Examples of
Heavy-Tailed Distributions 277 20.4 Divergence Measures 280 20.5 Hypothesis
Testing 284 20.6 Simulations 286 20.7 Conclusion 287 References 287 21
Robust Inference based on Divergences in Reliability Systems 290 Abhik
Ghosh, Avijit Maji and Ayanendranath Basu 21.1 Introduction 290 21.2 The
Power Divergence (PD) Family 291 21.3 Density Power Divergence (DPD) and
Parametric Inference 296 21.4 A Generalized Form: The S-Divergence 301 21.5
Applications 304 21.6 Conclusion 306 References 306 22 COM-Poisson Cure
Rate Models and Associated Likelihood-based Inference with Exponential and
Weibull Lifetimes 308 N. Balakrishnan and Suvra Pal 22.1 Introduction 308
22.2 Role of Cure Rate Models in Reliability 310 22.3 The COM-Poisson Cure
Rate Model 310 22.4 Data and the Likelihood 311 22.5 EM Algorithm 312 22.6
Standard Errors and Asymptotic Confidence Intervals 314 22.7 Exponential
Lifetime Distribution 314 22.8 Weibull Lifetime Distribution 322 22.9
Analysis of Cutaneous Melanoma Data 334 22.10 Conclusion 337 22.A1 Appendix
A1: E-Step and M-Step Formulas for Exponential Lifetimes 337 22.A2 Appendix
A2: E-Step and M-Step Formulas for Weibull Lifetimes 341 22.B1 Appendix B1:
Observed Information Matrix for Exponential Lifetimes 344 22.B2 Appendix
B2: Observed Information Matrix for Weibull Lifetimes 346 References 347 23
Exponential Expansions for Perturbed Discrete Time Renewal Equations 349
Dmitrii Silvestrov and Mikael Petersson 23.1 Introduction 349 23.2
Asymptotic Results 350 23.3 Proofs 353 23.4 Discrete Time Regenerative
Processes 358 23.5 Queuing and Risk Applications 359 References 361 24 On
Generalized Extreme Shock Models under Renewal Shock Processes 363 Ji Hwan
Cha and Maxim Finkelstein 24.1 Introduction 363 24.2 Generalized Extreme
Shock Models 364 24.3 Specific Models 367 24.4 Conclusion 373
Acknowledgements 373 References 373 Part V SYSTEMABILITY,
PHYSICS-OF-FAILURE AND RELIABILITY DEMONSTRATION 25 Systemability Theory
and its Applications 377 Hoang Pham 25.1 Introduction 377 25.2
Systemability Measures 378 25.3 Systemability Analysis of k-out-of-n
Systems 379 25.4 Systemability Function Approximation 380 25.5
Systemability with Loglog Distribution 383 25.6 Sensitivity Analysis 384
25.7 Applications: Red Light Camera Systems 385 25.8 Conclusion 387
References 387 26 Physics-of-Failure based Reliability Engineering 389
Pedro O. Quintero and Michael Pecht 26.1 Introduction 389 26.2
Physics-of-Failure-based Reliability Assessment 393 26.3 Uses of
Physics-of-Failure 398 26.4 Conclusion 400 References 400 27 Accelerated
Testing: Effect of Variance in Field Environmental Conditions on the
Demonstrated Reliability 403 Andre Kleyner 27.1 Introduction 403 27.2
Accelerated Testing and Field Stress Variation 404 27.3 Case Study:
Reliability Demonstration Using Temperature Cycling Test 405 27.4
Conclusion 408 References 408 Index 409
Remembering Boris Gnedenko xvii List of Contributors xxv Preface xxix
Acknowledgements xxxv Part I DEGRADATION ANALYSIS, MULTI-STATE AND
CONTINUOUS-STATE SYSTEM RELIABILITY 1 Methods of Solutions of Inhomogeneous
Continuous Time Markov Chains for Degradation Process Modeling 3 Yan-Fu Li,
Enrico Zio and Yan-Hui Lin 1.1 Introduction 3 1.2 Formalism of ICTMC 4 1.3
Numerical Solution Techniques 5 1.4 Examples 10 1.5 Comparisons of the
Methods and Guidelines of Utilization 13 1.6 Conclusion 15 References 15 2
Multistate Degradation and Condition Monitoring for Devices with Multiple
Independent Failure Modes 17 Ramin Moghaddass and Ming J. Zuo 2.1
Introduction 17 2.2 Multistate Degradation and Multiple Independent Failure
Modes 19 2.3 Parameter Estimation 23 2.4 Important Reliability Measures of
a Condition-Monitored Device 25 2.5 Numerical Example 27 2.6 Conclusion 28
Acknowledgements 30 References 30 3 Time Series Regression with Exponential
Errors for Accelerated Testing and Degradation Tracking 32 Nozer D.
Singpurwalla 3.1 Introduction 32 3.2 Preliminaries: Statement of the
Problem 33 3.3 Estimation and Prediction by Least Squares 34 3.4 Estimation
and Prediction by MLE 35 3.5 The Bayesian Approach: The Predictive
Distribution 37 Acknowledgements 42 References 42 4 Inverse Lz-Transform
for a Discrete-State Continuous-Time Markov Process and Its Application to
Multi-State System Reliability Analysis 43 Anatoly Lisnianski and Yi Ding
4.1 Introduction 43 4.2 Inverse Lz-Transform: Definitions and Computational
Procedure 44 4.3 Application of Inverse Lz-Transform to MSS Reliability
Analysis 50 4.4 Numerical Example 52 4.5 Conclusion 57 References 58 5
OntheLz-Transform Application for Availability Assessment of an Aging
Multi-State Water Cooling System for Medical Equipment 59 Ilia Frenkel,
Anatoly Lisnianski and Lev Khvatskin 5.1 Introduction 59 5.2 Brief
Description of the Lz-Transform Method 61 5.3 Multi-state Model of the
Water Cooling System for the MRI Equipment 62 5.4 Availability Calculation
75 5.5 Conclusion 76 Acknowledgments 76 References 77 6 Combined Clustering
and Lz-Transform Technique to Reduce the Computational Complexity of a
Multi-State System Reliability Evaluation 78 Yi Ding 6.1 Introduction 78
6.2 The Lz-Transform for Dynamic Reliability Evaluation for MSS 79 6.3
Clustering Composition Operator in the Lz-Transform 81 6.4 Computational
Procedures 83 6.5 Numerical Example 83 6.6 Conclusion 85 References 85 7
Sliding Window Systems with Gaps 87 Gregory Levitin 7.1 Introduction 87 7.2
The Models 89 7.3 Reliability Evaluation Technique 91 7.4 Conclusion 96
References 96 8 Development of Reliability Measures Motivated by Fuzzy Sets
for Systems with Multi- or Infinite-States 98 Zhaojun (Steven) Li and
Kailash C. Kapur 8.1 Introduction 98 8.2 Models for Components and Systems
Using Fuzzy Sets 100 8.3 Fuzzy Reliability for Systems with Continuous or
Infinite States 103 8.4 Dynamic Fuzzy Reliability 104 8.5 System Fuzzy
Reliability 110 8.6 Examples and Applications 111 8.7 Conclusion 117
References 118 9 Imperatives for Performability Design in the Twenty-First
Century 119 Krishna B. Misra 9.1 Introduction 119 9.2 Strategies for
Sustainable Development 120 9.3 Reappraisal of the Performance of Products
and Systems 124 9.4 Dependability and Environmental Risk are Interdependent
126 9.5 Performability: An Appropriate Measure of Performance 126 9.6
Towards Dependable and Sustainable Designs 129 9.7 Conclusion 130
References 130 Part II NETWORKS AND LARGE-SCALE SYSTEMS 10 Network
Reliability Calculations Based on Structural Invariants 135 Ilya B.
Gertsbakh and Yoseph Shpungin 10.1 First Invariant: D-Spectrum, Signature
135 10.2 Second Invariant: Importance Spectrum. Birnbaum Importance Measure
(BIM) 139 10.3 Example: Reliability of a Road Network 141 10.4 Third
Invariant: Border States 142 10.5 Monte Carlo to Approximate the Invariants
144 10.6 Conclusion 146 References 146 11 Performance and Availability
Evaluation of IMS-Based Core Networks 148 Kishor S. Trivedi, Fabio
Postiglione and Xiaoyan Yin 11.1 Introduction 148 11.2 IMS-Based Core
Network Description 149 11.3 Analytic Models for Independent Software
Recovery 151 11.4 Analytic Models for Recovery with Dependencies 155 11.5
Redundancy Optimization 158 11.6 Numerical Results 159 11.7 Conclusion 165
References 165 12 Reliability and Probability of First Occurred Failure for
Discrete-Time Semi-Markov Systems 167 Stylianos Georgiadis, Nikolaos
Limnios and Irene Votsi 12.1 Introduction 167 12.2 Discrete-Time
Semi-Markov Model 168 12.3 Reliability and Probability of First Occurred
Failure 170 12.4 Nonparametric Estimation of Reliability Measures 172 12.5
Numerical Application 176 12.6 Conclusion 178 References 179 13
Single-Source Epidemic Process in a System of Two Interconnected Networks
180 Ilya B. Gertsbakh and Yoseph Shpungin 13.1 Introduction 180 13.2
Failure Process and the Distribution of the Number of Failed Nodes 181 13.3
Network Failure Probabilities 184 13.4 Example 185 13.5 Conclusion 187 13.A
Appendix D: Spectrum (Signature) 188 References 189 Part III MAINTENANCE
MODELS 14 Comparisons of Periodic and Random Replacement Policies 193
Xufeng Zhao and Toshio Nakagawa 14.1 Introduction 193 14.2 Four Policies
195 14.3 Comparisons of Optimal Policies 197 14.4 Numerical Examples 1 199
14.5 Comparisons of Policies with Different Replacement Costs 201 14.6
Numerical Examples 2 202 14.7 Conclusion 203 Acknowledgements 204
References 204 15 Random Evolution of Degradation and Occurrences of Words
in Random Sequences of Letters 205 Emilio De Santis and Fabio Spizzichino
15.1 Introduction 205 15.2 Waiting Times to Words' Occurrences 206 15.3
Some Reliability-Maintenance Models 209 15.4 Waiting Times to Occurrences
of Words and Stochastic Comparisons for Degradation 213 15.5 Conclusions
216 Acknowledgements 217 References 217 16 Occupancy Times for Markov and
Semi-Markov Models in Systems Reliability 218 Alan G. Hawkes, Lirong Cui
and Shijia Du 16.1 Introduction 218 16.2 Markov Models for Systems
Reliability 220 16.3 Semi-Markov Models 222 16.4 Time Interval Omission 225
16.5 Numerical Examples 226 16.6 Conclusion 229 Acknowledgements 229
References 229 17 A Practice of Imperfect Maintenance Model Selection for
Diesel Engines 231 Yu Liu, Hong-Zhong Huang, Shun-Peng Zhu and Yan-Feng Li
17.1 Introduction 231 17.2 Review of Imperfect Maintenance Model Selection
Method 233 17.3 Application to Preventive Maintenance Scheduling of Diesel
Engines 236 17.4 Conclusion 244 Acknowledgment 245 References 245 18
Reliability of Warm Standby Systems with Imperfect Fault Coverage 246 Rui
Peng, Ola Tannous, Liudong Xing and Min Xie 18.1 Introduction 246 18.2
Literature Review 247 18.3 The BDD-Based Approach 250 18.4 Conclusion 253
Acknowledgments 254 References 254 Part IV STATISTICAL INFERENCE IN
RELIABILITY 19 On the Validity of the Weibull-Gnedenko Model 259 Vilijandas
Bagdonavi¡cius, Mikhail Nikulin and Ruta Levuliene 19.1 Introduction 259
19.2 Integrated Likelihood Ratio Test 261 19.3 Tests based on the
Difference of Non-Parametric and Parametric Estimators of the Cumulative
Distribution Function 264 19.4 Tests based on Spacings 266 19.5 Chi-Squared
Tests 267 19.6 Correlation Test 269 19.7 Power Comparison 269 19.8
Conclusion 272 References 272 20 Statistical Inference for Heavy-Tailed
Distributions in Reliability Systems 273 Ilia Vonta and Alex Karagrigoriou
20.1 Introduction 273 20.2 Heavy-Tailed Distributions 274 20.3 Examples of
Heavy-Tailed Distributions 277 20.4 Divergence Measures 280 20.5 Hypothesis
Testing 284 20.6 Simulations 286 20.7 Conclusion 287 References 287 21
Robust Inference based on Divergences in Reliability Systems 290 Abhik
Ghosh, Avijit Maji and Ayanendranath Basu 21.1 Introduction 290 21.2 The
Power Divergence (PD) Family 291 21.3 Density Power Divergence (DPD) and
Parametric Inference 296 21.4 A Generalized Form: The S-Divergence 301 21.5
Applications 304 21.6 Conclusion 306 References 306 22 COM-Poisson Cure
Rate Models and Associated Likelihood-based Inference with Exponential and
Weibull Lifetimes 308 N. Balakrishnan and Suvra Pal 22.1 Introduction 308
22.2 Role of Cure Rate Models in Reliability 310 22.3 The COM-Poisson Cure
Rate Model 310 22.4 Data and the Likelihood 311 22.5 EM Algorithm 312 22.6
Standard Errors and Asymptotic Confidence Intervals 314 22.7 Exponential
Lifetime Distribution 314 22.8 Weibull Lifetime Distribution 322 22.9
Analysis of Cutaneous Melanoma Data 334 22.10 Conclusion 337 22.A1 Appendix
A1: E-Step and M-Step Formulas for Exponential Lifetimes 337 22.A2 Appendix
A2: E-Step and M-Step Formulas for Weibull Lifetimes 341 22.B1 Appendix B1:
Observed Information Matrix for Exponential Lifetimes 344 22.B2 Appendix
B2: Observed Information Matrix for Weibull Lifetimes 346 References 347 23
Exponential Expansions for Perturbed Discrete Time Renewal Equations 349
Dmitrii Silvestrov and Mikael Petersson 23.1 Introduction 349 23.2
Asymptotic Results 350 23.3 Proofs 353 23.4 Discrete Time Regenerative
Processes 358 23.5 Queuing and Risk Applications 359 References 361 24 On
Generalized Extreme Shock Models under Renewal Shock Processes 363 Ji Hwan
Cha and Maxim Finkelstein 24.1 Introduction 363 24.2 Generalized Extreme
Shock Models 364 24.3 Specific Models 367 24.4 Conclusion 373
Acknowledgements 373 References 373 Part V SYSTEMABILITY,
PHYSICS-OF-FAILURE AND RELIABILITY DEMONSTRATION 25 Systemability Theory
and its Applications 377 Hoang Pham 25.1 Introduction 377 25.2
Systemability Measures 378 25.3 Systemability Analysis of k-out-of-n
Systems 379 25.4 Systemability Function Approximation 380 25.5
Systemability with Loglog Distribution 383 25.6 Sensitivity Analysis 384
25.7 Applications: Red Light Camera Systems 385 25.8 Conclusion 387
References 387 26 Physics-of-Failure based Reliability Engineering 389
Pedro O. Quintero and Michael Pecht 26.1 Introduction 389 26.2
Physics-of-Failure-based Reliability Assessment 393 26.3 Uses of
Physics-of-Failure 398 26.4 Conclusion 400 References 400 27 Accelerated
Testing: Effect of Variance in Field Environmental Conditions on the
Demonstrated Reliability 403 Andre Kleyner 27.1 Introduction 403 27.2
Accelerated Testing and Field Stress Variation 404 27.3 Case Study:
Reliability Demonstration Using Temperature Cycling Test 405 27.4
Conclusion 408 References 408 Index 409
Acknowledgements xxxv Part I DEGRADATION ANALYSIS, MULTI-STATE AND
CONTINUOUS-STATE SYSTEM RELIABILITY 1 Methods of Solutions of Inhomogeneous
Continuous Time Markov Chains for Degradation Process Modeling 3 Yan-Fu Li,
Enrico Zio and Yan-Hui Lin 1.1 Introduction 3 1.2 Formalism of ICTMC 4 1.3
Numerical Solution Techniques 5 1.4 Examples 10 1.5 Comparisons of the
Methods and Guidelines of Utilization 13 1.6 Conclusion 15 References 15 2
Multistate Degradation and Condition Monitoring for Devices with Multiple
Independent Failure Modes 17 Ramin Moghaddass and Ming J. Zuo 2.1
Introduction 17 2.2 Multistate Degradation and Multiple Independent Failure
Modes 19 2.3 Parameter Estimation 23 2.4 Important Reliability Measures of
a Condition-Monitored Device 25 2.5 Numerical Example 27 2.6 Conclusion 28
Acknowledgements 30 References 30 3 Time Series Regression with Exponential
Errors for Accelerated Testing and Degradation Tracking 32 Nozer D.
Singpurwalla 3.1 Introduction 32 3.2 Preliminaries: Statement of the
Problem 33 3.3 Estimation and Prediction by Least Squares 34 3.4 Estimation
and Prediction by MLE 35 3.5 The Bayesian Approach: The Predictive
Distribution 37 Acknowledgements 42 References 42 4 Inverse Lz-Transform
for a Discrete-State Continuous-Time Markov Process and Its Application to
Multi-State System Reliability Analysis 43 Anatoly Lisnianski and Yi Ding
4.1 Introduction 43 4.2 Inverse Lz-Transform: Definitions and Computational
Procedure 44 4.3 Application of Inverse Lz-Transform to MSS Reliability
Analysis 50 4.4 Numerical Example 52 4.5 Conclusion 57 References 58 5
OntheLz-Transform Application for Availability Assessment of an Aging
Multi-State Water Cooling System for Medical Equipment 59 Ilia Frenkel,
Anatoly Lisnianski and Lev Khvatskin 5.1 Introduction 59 5.2 Brief
Description of the Lz-Transform Method 61 5.3 Multi-state Model of the
Water Cooling System for the MRI Equipment 62 5.4 Availability Calculation
75 5.5 Conclusion 76 Acknowledgments 76 References 77 6 Combined Clustering
and Lz-Transform Technique to Reduce the Computational Complexity of a
Multi-State System Reliability Evaluation 78 Yi Ding 6.1 Introduction 78
6.2 The Lz-Transform for Dynamic Reliability Evaluation for MSS 79 6.3
Clustering Composition Operator in the Lz-Transform 81 6.4 Computational
Procedures 83 6.5 Numerical Example 83 6.6 Conclusion 85 References 85 7
Sliding Window Systems with Gaps 87 Gregory Levitin 7.1 Introduction 87 7.2
The Models 89 7.3 Reliability Evaluation Technique 91 7.4 Conclusion 96
References 96 8 Development of Reliability Measures Motivated by Fuzzy Sets
for Systems with Multi- or Infinite-States 98 Zhaojun (Steven) Li and
Kailash C. Kapur 8.1 Introduction 98 8.2 Models for Components and Systems
Using Fuzzy Sets 100 8.3 Fuzzy Reliability for Systems with Continuous or
Infinite States 103 8.4 Dynamic Fuzzy Reliability 104 8.5 System Fuzzy
Reliability 110 8.6 Examples and Applications 111 8.7 Conclusion 117
References 118 9 Imperatives for Performability Design in the Twenty-First
Century 119 Krishna B. Misra 9.1 Introduction 119 9.2 Strategies for
Sustainable Development 120 9.3 Reappraisal of the Performance of Products
and Systems 124 9.4 Dependability and Environmental Risk are Interdependent
126 9.5 Performability: An Appropriate Measure of Performance 126 9.6
Towards Dependable and Sustainable Designs 129 9.7 Conclusion 130
References 130 Part II NETWORKS AND LARGE-SCALE SYSTEMS 10 Network
Reliability Calculations Based on Structural Invariants 135 Ilya B.
Gertsbakh and Yoseph Shpungin 10.1 First Invariant: D-Spectrum, Signature
135 10.2 Second Invariant: Importance Spectrum. Birnbaum Importance Measure
(BIM) 139 10.3 Example: Reliability of a Road Network 141 10.4 Third
Invariant: Border States 142 10.5 Monte Carlo to Approximate the Invariants
144 10.6 Conclusion 146 References 146 11 Performance and Availability
Evaluation of IMS-Based Core Networks 148 Kishor S. Trivedi, Fabio
Postiglione and Xiaoyan Yin 11.1 Introduction 148 11.2 IMS-Based Core
Network Description 149 11.3 Analytic Models for Independent Software
Recovery 151 11.4 Analytic Models for Recovery with Dependencies 155 11.5
Redundancy Optimization 158 11.6 Numerical Results 159 11.7 Conclusion 165
References 165 12 Reliability and Probability of First Occurred Failure for
Discrete-Time Semi-Markov Systems 167 Stylianos Georgiadis, Nikolaos
Limnios and Irene Votsi 12.1 Introduction 167 12.2 Discrete-Time
Semi-Markov Model 168 12.3 Reliability and Probability of First Occurred
Failure 170 12.4 Nonparametric Estimation of Reliability Measures 172 12.5
Numerical Application 176 12.6 Conclusion 178 References 179 13
Single-Source Epidemic Process in a System of Two Interconnected Networks
180 Ilya B. Gertsbakh and Yoseph Shpungin 13.1 Introduction 180 13.2
Failure Process and the Distribution of the Number of Failed Nodes 181 13.3
Network Failure Probabilities 184 13.4 Example 185 13.5 Conclusion 187 13.A
Appendix D: Spectrum (Signature) 188 References 189 Part III MAINTENANCE
MODELS 14 Comparisons of Periodic and Random Replacement Policies 193
Xufeng Zhao and Toshio Nakagawa 14.1 Introduction 193 14.2 Four Policies
195 14.3 Comparisons of Optimal Policies 197 14.4 Numerical Examples 1 199
14.5 Comparisons of Policies with Different Replacement Costs 201 14.6
Numerical Examples 2 202 14.7 Conclusion 203 Acknowledgements 204
References 204 15 Random Evolution of Degradation and Occurrences of Words
in Random Sequences of Letters 205 Emilio De Santis and Fabio Spizzichino
15.1 Introduction 205 15.2 Waiting Times to Words' Occurrences 206 15.3
Some Reliability-Maintenance Models 209 15.4 Waiting Times to Occurrences
of Words and Stochastic Comparisons for Degradation 213 15.5 Conclusions
216 Acknowledgements 217 References 217 16 Occupancy Times for Markov and
Semi-Markov Models in Systems Reliability 218 Alan G. Hawkes, Lirong Cui
and Shijia Du 16.1 Introduction 218 16.2 Markov Models for Systems
Reliability 220 16.3 Semi-Markov Models 222 16.4 Time Interval Omission 225
16.5 Numerical Examples 226 16.6 Conclusion 229 Acknowledgements 229
References 229 17 A Practice of Imperfect Maintenance Model Selection for
Diesel Engines 231 Yu Liu, Hong-Zhong Huang, Shun-Peng Zhu and Yan-Feng Li
17.1 Introduction 231 17.2 Review of Imperfect Maintenance Model Selection
Method 233 17.3 Application to Preventive Maintenance Scheduling of Diesel
Engines 236 17.4 Conclusion 244 Acknowledgment 245 References 245 18
Reliability of Warm Standby Systems with Imperfect Fault Coverage 246 Rui
Peng, Ola Tannous, Liudong Xing and Min Xie 18.1 Introduction 246 18.2
Literature Review 247 18.3 The BDD-Based Approach 250 18.4 Conclusion 253
Acknowledgments 254 References 254 Part IV STATISTICAL INFERENCE IN
RELIABILITY 19 On the Validity of the Weibull-Gnedenko Model 259 Vilijandas
Bagdonavi¡cius, Mikhail Nikulin and Ruta Levuliene 19.1 Introduction 259
19.2 Integrated Likelihood Ratio Test 261 19.3 Tests based on the
Difference of Non-Parametric and Parametric Estimators of the Cumulative
Distribution Function 264 19.4 Tests based on Spacings 266 19.5 Chi-Squared
Tests 267 19.6 Correlation Test 269 19.7 Power Comparison 269 19.8
Conclusion 272 References 272 20 Statistical Inference for Heavy-Tailed
Distributions in Reliability Systems 273 Ilia Vonta and Alex Karagrigoriou
20.1 Introduction 273 20.2 Heavy-Tailed Distributions 274 20.3 Examples of
Heavy-Tailed Distributions 277 20.4 Divergence Measures 280 20.5 Hypothesis
Testing 284 20.6 Simulations 286 20.7 Conclusion 287 References 287 21
Robust Inference based on Divergences in Reliability Systems 290 Abhik
Ghosh, Avijit Maji and Ayanendranath Basu 21.1 Introduction 290 21.2 The
Power Divergence (PD) Family 291 21.3 Density Power Divergence (DPD) and
Parametric Inference 296 21.4 A Generalized Form: The S-Divergence 301 21.5
Applications 304 21.6 Conclusion 306 References 306 22 COM-Poisson Cure
Rate Models and Associated Likelihood-based Inference with Exponential and
Weibull Lifetimes 308 N. Balakrishnan and Suvra Pal 22.1 Introduction 308
22.2 Role of Cure Rate Models in Reliability 310 22.3 The COM-Poisson Cure
Rate Model 310 22.4 Data and the Likelihood 311 22.5 EM Algorithm 312 22.6
Standard Errors and Asymptotic Confidence Intervals 314 22.7 Exponential
Lifetime Distribution 314 22.8 Weibull Lifetime Distribution 322 22.9
Analysis of Cutaneous Melanoma Data 334 22.10 Conclusion 337 22.A1 Appendix
A1: E-Step and M-Step Formulas for Exponential Lifetimes 337 22.A2 Appendix
A2: E-Step and M-Step Formulas for Weibull Lifetimes 341 22.B1 Appendix B1:
Observed Information Matrix for Exponential Lifetimes 344 22.B2 Appendix
B2: Observed Information Matrix for Weibull Lifetimes 346 References 347 23
Exponential Expansions for Perturbed Discrete Time Renewal Equations 349
Dmitrii Silvestrov and Mikael Petersson 23.1 Introduction 349 23.2
Asymptotic Results 350 23.3 Proofs 353 23.4 Discrete Time Regenerative
Processes 358 23.5 Queuing and Risk Applications 359 References 361 24 On
Generalized Extreme Shock Models under Renewal Shock Processes 363 Ji Hwan
Cha and Maxim Finkelstein 24.1 Introduction 363 24.2 Generalized Extreme
Shock Models 364 24.3 Specific Models 367 24.4 Conclusion 373
Acknowledgements 373 References 373 Part V SYSTEMABILITY,
PHYSICS-OF-FAILURE AND RELIABILITY DEMONSTRATION 25 Systemability Theory
and its Applications 377 Hoang Pham 25.1 Introduction 377 25.2
Systemability Measures 378 25.3 Systemability Analysis of k-out-of-n
Systems 379 25.4 Systemability Function Approximation 380 25.5
Systemability with Loglog Distribution 383 25.6 Sensitivity Analysis 384
25.7 Applications: Red Light Camera Systems 385 25.8 Conclusion 387
References 387 26 Physics-of-Failure based Reliability Engineering 389
Pedro O. Quintero and Michael Pecht 26.1 Introduction 389 26.2
Physics-of-Failure-based Reliability Assessment 393 26.3 Uses of
Physics-of-Failure 398 26.4 Conclusion 400 References 400 27 Accelerated
Testing: Effect of Variance in Field Environmental Conditions on the
Demonstrated Reliability 403 Andre Kleyner 27.1 Introduction 403 27.2
Accelerated Testing and Field Stress Variation 404 27.3 Case Study:
Reliability Demonstration Using Temperature Cycling Test 405 27.4
Conclusion 408 References 408 Index 409