Applied Reliability Engineering and Risk Analysis Front Cover

Applied Reliability Engineering and Risk Analysis

  • Length: 456 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2013-12-04
  • ISBN-10: 1118539427
  • ISBN-13: 9781118539422
  • Sales Rank: #6101617 (See Top 100 Books)
Description

Applied Reliability Engineering and Risk Analysis: Probabilistic Models and Statistical Inference (Quality and Reliability Engineering Series)

This book presents the latest developments in the field of reliability science focusing on applied reliability, probabilistic models and risk analysis.  It provides readers with the most up-to-date developments in this field and consolidates research activities in several areas of applied reliability engineering. The publication is timed to commemorate Boris Gnedenko’s centennial by bringing together leading researchers, scientists, and practitioners in the field of Prof. Gnednko’s expertise.  The Introduction, written by Prof. Igor Ushakov, a personal friend and a colleague of Boris Gnedenko, explains the significant impact and contribution Gnedenko’s work made on the reliability theory and the modern reliability practice. The book covers conventional and contemporary (recently emerged) topics in reliability science, which have seen extended research activities in the recent years.  These topics include: degradation analysis and multi-state system reliability; networks and large scale systems; maintenance models; statistical inference in reliability, and; physics of failures and reliability demonstration. All of these topics present a great interest to researchers and practitioners, having been extensively researched in the past years and covered at a large number of international conferences and in a multitude of journal articles. This book pulls together this information with a coherent flow of chapters, and is written by the lead scientists, researchers and practitioners in their respective fields. Logically divided into five sections, each contains several chapters covering theoretical and practical issues, while case studies support the topics under discussion.

Table of Contents

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
2 Multistate Degradation and Condition Monitoring for Devices with Multiple Independent Failure Modes 17
3 Time Series Regression with Exponential Errors for Accelerated Testing and Degradation Tracking 32
4 Inverse Lz-Transform for a Discrete-State Continuous-Time Markov Process and Its Application to Multi-State System Reliability Analysis 43
5 OntheLz-Transform Application for Availability Assessment of an Aging Multi-State Water Cooling System for Medical Equipment 59
6 Combined Clustering and Lz-Transform Technique to Reduce the Computational Complexity of a Multi-State System Reliability Evaluation 78
7 Sliding Window Systems with Gaps 87
8 Development of Reliability Measures Motivated by Fuzzy Sets for Systems with Multi- or Infinite-States 98
9 Imperatives for Performability Design in the Twenty-First Century 119

Part II NETWORKS AND LARGE-SCALE SYSTEMS
10 Network Reliability Calculations Based on Structural Invariants 135
11 Performance and Availability Evaluation of IMS-Based Core Networks 148
12 Reliability and Probability of First Occurred Failure for Discrete-Time Semi-Markov Systems 167
13 Single-Source Epidemic Process in a System of Two Interconnected Networks 180

Part III MAINTENANCE MODELS
14 Comparisons of Periodic and Random Replacement Policies 193
15 Random Evolution of Degradation and Occurrences of Words in Random Sequences of Letters 205
16 Occupancy Times for Markov and Semi-Markov Models in Systems Reliability 218
17 A Practice of Imperfect Maintenance Model Selection for Diesel Engines 231
18 Reliability of Warm Standby Systems with Imperfect Fault Coverage 246

Part IV STATISTICAL INFERENCE IN RELIABILITY
19 On the Validity of the Weibull-Gnedenko Model 259
20 Statistical Inference for Heavy-Tailed Distributions in Reliability Systems 273
21 Robust Inference based on Divergences in Reliability Systems 290
22 COM-Poisson Cure Rate Models and Associated Likelihood-based Inference with Exponential and Weibull Lifetimes 308
23 Exponential Expansions for Perturbed Discrete Time Renewal Equations 349
24 On Generalized Extreme Shock Models under Renewal Shock Processes 363

Part V SYSTEMABILITY, PHYSICS-OF-FAILURE AND RELIABILITY DEMONSTRATION
25 Systemability Theory and its Applications 377
26 Physics-of-Failure based Reliability Engineering 389
27 Accelerated Testing: Effect of Variance in Field Environmental Conditions on the Demonstrated Reliability 403

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