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Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers - Anatoly Lisnianski

Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers


Published: 8th August 2010
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Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers presents a comprehensive, up-to-date description of multi-state system (MSS) reliability as a natural extension of classical binary-state reliability. It presents all essential theoretical achievements in the field, but is also practically oriented.

New theoretical issues are described, including:

Markov and semi-Markov processes methods combined with universal generating function technique;

statistical data processing for MSSs;

reliability analysis of aging MSSs;

methods for cost-reliability and cost-availability analysis of MSSs; and

main definitions and concepts of fuzzy MSS.

Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also discusses life cycle cost analysis and practical optimal decision making for real world MSSs. Numerous examples are included in each section in order to illustrate mathematical tools. Besides these examples, real world MSSs (such as power generating and transmission systems, air-conditioning systems, production systems, etc.) are considered as case studies.

Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also describes basic concepts of MSS, MSS reliability measures and tools for MSS reliability assessment and optimization. It is a self-contained study resource and does not require prior knowledge from its readers, making the book attractive for researchers as well as for practical engineers and industrial managers.

MATLAB codes provided in the appendix of the book enhance its usefulness for a serious minded researcher who likes to go in depth into the study of MSS.

The book is a stand alone resource and does not require any prior knowledge on the part of a reader. This feature is likely to be appreciated by beginners who are being initiated to MSS reliability analysis. This feature will be particularly useful to the practicing engineers and managers who do not have time to look around and search all the supporting resources. The reviewer feels that this book would also be found useful for researchers, students and teachers.

Krishna B. Misra, International Journal of Performability Engineering

Multi-state Systems in Nature and in Engineeringp. 1
Multi-state Systems in the Real World: General Conceptsp. 1
Main Definitions and Propertiesp. 8
Generic Multi-state System Modelp. 8
Main Properties of Multi-state Systemsp. 13
Multi-state System Reliability and Its Measuresp. 16
Acceptable and Unacceptable States. Failure Criteriap. 16
Relevancy and Coherency in Multi-state System Reliability Contextp. 17
Multi-state Systems Reliability Measuresp. 18
Referencesp. 27
Modern Stochastic Process Methods for Multi-state System Reliability Assessmentp. 29
General Concepts of Stochastic Process Theoryp. 30
Markov Models: Discrete-time Markov Chainsp. 34
Basic Definitions and Propertiesp. 34
Computation of n-step Transition Probabilities and State Probabilitiesp. 36
Markov Models: Continuous-time Markov Chainsp. 40
Basic Definitions and Propertiesp. 40
Markov Models for the Evaluating Reliability of Multi-state Elementsp. 48
Markov Models for Evaluating the Reliability of Multi-state Systemsp. 66
Markov Reward Modelsp. 79
Basic Definition and Model Descriptionp. 79
Computation of Multi-state System Reliability Measures Using Markov Reward Modelsp. 84
Semi-Markov Modelsp. 99
Embedded Markov Chain and Definition of Semi-Markov Processp. 100
Evaluation of Reliability Indices Based on Semi-Markov Processesp. 105
Referencesp. 113
Statistical Analysis of Reliability Data for Multi-state Systemsp. 117
Basic Concepts of Statistical Estimation Theoryp. 117
Properties of Estimatorsp. 118
Main Estimation Methodsp. 120
Classical Parametric Estimation for Binary-state Systemp. 127
Basic Considerationsp. 127
Exponential Distribution Point Estimationp. 128
Interval Estimation for Exponential Distributionp. 131
Estimation of Transition Intensities for via Output Performance Observationsp. 132
Multi-state Markov Model and Observed Reliability Data. Problem Formulationp. 132
Method Descriptionp. 135
Algorithm for Point Estimation of Transition Intensities for Multi-state Systemsp. 138
Interval Estimation of Transition Intensities for Multi-state Systemp. 139
Referencesp. 142
Universal Generating Function Methodp. 143
Mathematical Fundamentalsp. 143
Generating Functionsp. 144
Moment Generating Functions and the z-transformp. 148
Universal Generating Operator and Universal Generating Functionp. 152
Generalized Universal Generating Operatorp. 155
Universal Generating Function Associated with Stochastic Processesp. 158
Universal Generating Function Techniquep. 159
Like-term Collection and Recursive Procedurep. 159
Evaluating Multi-state System Reliability Indices Using Universal Generating Functionsp. 162
Properties of Composition Operatorsp. 167
Universal Generating Function of Subsystems with Elements Connected in Seriesp. 170
Universal Generating Function of Subsystems with Elements Connected in Parallelp. 172
Universal Generating Function of Series-parallel Systemsp. 175
Universal Generating Function of Systems with Bridge Structurep. 178
Importance and Sensitivity Analysis Using Universal Generating Functionp. 183
Estimating Boundary Points for Continuous-state System Reliability Measuresp. 188
Discrete Approximationp. 189
Boundary Point Estimationp. 193
Referencesp. 198
Combined Universal Generating Function and Stochastic Process Methodp. 201
Method Descriptionp. 202
Performance Stochastic Process for Multi-state Elementp. 202
Multi-state System Reliability Evaluationp. 207
Redundancy Analysis for Multi-state Systemsp. 214
Introductionp. 214
Problem Formulationp. 216
Model Descriptionp. 218
Algorithm for Universal Generating Function Computation for Entire Multi-state Systemp. 226
Reliability Measures Computation for Entire Multi-state Systemp. 228
Case Studiesp. 228
Referencesp. 234
Reliability-associated Cost Assessment and Management Decisions for Multi-state Systemsp. 237
Basic Life Cycle Cost Conceptp. 238
Reliability-associated Cost and Practical Cost-reliability Analysisp. 242
Case Study 1: Air Conditioning Systemp. 243
Case Study 2: Feed Water Pumps for Power Generating Unitp. 257
Practical Cost-reliability Optimization Problems for Multi-state Systemsp. 265
Multi-state System Structure Optimizationp. 265
Single-stage Expansion of Multi-state Systemsp. 270
Referencesp. 272
Aging Multi-state Systemsp. 273
Markov Model and Markov Reward Model for Increasing Failure Rate Functionp. 273
Case Study: Multi-state Power Generating Unitp. 275
Numerical Methods for Reliability Computation for Aging Multi-state Systemp. 281
Bound Approximation of Increasing Failure Rate Functionp. 283
Availability Bounds for Increasing Failure Rate Functionp. 285
Total Expected Reward Bounds for Increasing Failure Rate Functionp. 287
Reliability-associated Cost Assessment for Aging Multi-state Systemp. 291
Case Study: Maintenance Investigation for Aging Air Conditioning Systemp. 293
Optimal Corrective Maintenance Contract Planning for Aging Multi-state Systemp. 299
Algorithm for Availability and Total Expected Cost Bound Estimationp. 301
Optimization Technique Using Genetic Algorithmsp. 302
Case Study: Optimal Corrective Maintenance Contract for Aging Air Conditioning Systemp. 303
Optimal Preventive Replacement Policy for Aging Multi-state Systemsp. 310
Problem Formulationp. 311
Implementing the Genetic Algorithmp. 313
Case Study: Optimal Preventive Maintenance for Aging Water Desalination Systemp. 315
Referencesp. 318
Fuzzy Multi-state System: General Definition and Reliability Assessmentp. 321
Introductionp. 321
Key Definitions and Concepts of a Fuzzy Multi-state Systemp. 323
Reliability Evaluation of Fuzzy Multi-state Systemsp. 336
Fuzzy Universal Generating Functions: Definitions and Propertiesp. 336
Availability Assessment for Fuzzy Multi-state Systemsp. 337
Fuzzy Universal Generating Function for Series-parallel Fuzzy Multi-state Systemsp. 338
Illustrative Examplesp. 343
Referencesp. 346
Heuristic Algorithms as a General Optimization Techniquep. 347
Introductionp. 347
Parameter Determination Problemsp. 355
Partition and Allocation Problemsp. 356
Mixed Partition and Parameter Determination Problemsp. 359
Sequencing Problemsp. 360
Determination of Solution Fitnessp. 362
Basic Genetic Algorithm Procedures and Reliability Applicationp. 364
Referencesp. 365
Parameter Estimation and Hypothesis Testing for Non-homogeneous Poisson Processp. 367
Homogeneous Poisson Processp. 367
Non-homogeneous Poisson Processp. 368
General Description of Non-homogeneous Poisson Processp. 368
Hypothesis Testingp. 370
Computer-intensive Procedure for Testing the Non-homogeneous Poisson Process Hypothesisp. 372
Referencesp. 375
MATLAB“ Codes for Examples and Case Study Calculationp. 377
Using MATLAB“ ODE Solversp. 377
MATLAB“ Code for Example 2.2p. 377
MATLAB“ Code for Example 2.3p. 378
MATLAB“ Code for Example 2.4p. 379
MATLAB“ Code for Air Conditioning System (Case Study 6.2.1)p. 381
Calculating Average Availabilityp. 381
Calculating Total Number of System Failuresp. 383
Calculating Mean Time lo System Failurep. 384
Calculating Probability of Failure-free Operationp. 386
MATLAB“ Code for Multi-state Power Generation Unit (Case Study 7.1.1)p. 387
Calculating Average Availabilityp. 387
Calculating Total Number of System Failuresp. 388
Calculating Reliability Functionp. 388
Referencesp. 389
Indexp. 391
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781849963190
ISBN-10: 1849963193
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 393
Published: 8th August 2010
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.5  x 2.54
Weight (kg): 1.66