Ilia B Frenkel: Applied Reliability Engineering and Risk Analysis, Gebunden

Inhaltsangabe

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 Invariant

Klappentext

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