{"product_id":"system-reliability-assessment-and-optimization-9781119265870","title":"System Reliability Assessment and Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book offers a comprehensive overview of recently developed methods for assessing and optimizing system reliability. It consists of two main parts, for treating assessment methods and optimization methods, respectively.\u003c\/p\u003e \u003cp\u003eThe first part covers methods of multi-state system reliability modelling and evaluation, Markov processes, Monte Carlo simulation and uncertainty analysis. The methods considered range from piecewise-deterministic Markov processes to belief function analysis. The second part covers optimization methods of mathematical programming and evolutionary algorithms, and problems of multi-objective optimization and optimization under uncertainty. The methods of this part range from non-dominated sorting genetic algorithm to robust optimization.\u003c\/p\u003e \u003cp\u003eThe book also includes the application of the assessment and optimization methods considered on real case studies, particularly with respect to the reliability assessment and optimization of renewable energy systems,\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eSeries Editor’s Foreword by \u003ci\u003eDr. Andre V. Kleyner\u003c\/i\u003e xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003eList of Abbreviations xx\u003c\/p\u003e \u003cp\u003eNotations xxii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I The Fundamentals 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Reliability Assessment 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Definitions of Reliability 3\u003c\/p\u003e \u003cp\u003e1.1.1 Probability of Survival 3\u003c\/p\u003e \u003cp\u003e1.2 Component Reliability Modeling 6\u003c\/p\u003e \u003cp\u003e1.2.1 Discrete Probability Distributions 6\u003c\/p\u003e \u003cp\u003e1.2.2 Continuous Probability Distributions 8\u003c\/p\u003e \u003cp\u003e1.2.3 Physics-of-Failure Equations 13\u003c\/p\u003e \u003cp\u003e1.3 System Reliability Modeling 15\u003c\/p\u003e \u003cp\u003e1.3.1 Series System 15\u003c\/p\u003e \u003cp\u003e1.3.2 Parallel System 16\u003c\/p\u003e \u003cp\u003e1.3.3 Series-parallel System 16\u003c\/p\u003e \u003cp\u003e1.3.4 K-out-of-n System 17\u003c\/p\u003e \u003cp\u003e1.3.5 Network System 18\u003c\/p\u003e \u003cp\u003e1.4 System Reliability Assessment Methods 18\u003c\/p\u003e \u003cp\u003e1.4.1 Path-set and Cut-set Method 18\u003c\/p\u003e \u003cp\u003e1.4.2 Decomposition and Factorization 19\u003c\/p\u003e \u003cp\u003e1.4.3 Binary Decision Diagram 19\u003c\/p\u003e \u003cp\u003e1.5 Exercises 20\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Optimization 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Optimization Problems 23\u003c\/p\u003e \u003cp\u003e2.1.1 Component Reliability Enhancement 23\u003c\/p\u003e \u003cp\u003e2.1.2 Redundancy Allocation 24\u003c\/p\u003e \u003cp\u003e2.1.3 Component Assignment 25\u003c\/p\u003e \u003cp\u003e2.1.4 Maintenance and Testing 26\u003c\/p\u003e \u003cp\u003e2.2 Optimization Methods 30\u003c\/p\u003e \u003cp\u003e2.2.1 Mathematical Programming 30\u003c\/p\u003e \u003cp\u003e2.2.2 Meta-heuristics 34\u003c\/p\u003e \u003cp\u003e2.3 Exercises 36\u003c\/p\u003e \u003cp\u003eReferences 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Reliability Techniques 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multi-State Systems (MSSs) 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Classical Multi-state Models 43\u003c\/p\u003e \u003cp\u003e3.2 Generalized Multi-state Models 45\u003c\/p\u003e \u003cp\u003e3.3 Time-dependent Multi-State Models 46\u003c\/p\u003e \u003cp\u003e3.4 Methods to Evaluate Multi-state System Reliability 48\u003c\/p\u003e \u003cp\u003e3.4.1 Methods Based on MPVs or MCVs 48\u003c\/p\u003e \u003cp\u003e3.4.2 Methods Derived from Binary State Reliability Assessment 48\u003c\/p\u003e \u003cp\u003e3.4.3 Universal Generating Function Approach 49\u003c\/p\u003e \u003cp\u003e3.4.4 Monte Carlo Simulation 50\u003c\/p\u003e \u003cp\u003e3.5 Exercises 51\u003c\/p\u003e \u003cp\u003eReferences 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Markov Processes 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Continuous Time Markov Chain (CMTC) 55\u003c\/p\u003e \u003cp\u003e4.2 In homogeneous Continuous Time Markov Chain 61\u003c\/p\u003e \u003cp\u003e4.3 Semi-Markov Process (SMP) 66\u003c\/p\u003e \u003cp\u003e4.4 Piecewise Deterministic Markov Process (PDMP) 74\u003c\/p\u003e \u003cp\u003e4.5 Exercises 82\u003c\/p\u003e \u003cp\u003eReferences 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Monte Carlo Simulation (MCS) for Reliability and Availability Assessment 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 87\u003c\/p\u003e \u003cp\u003e5.2 Random Variable Generation 87\u003c\/p\u003e \u003cp\u003e5.2.1 Random Number Generation 87\u003c\/p\u003e \u003cp\u003e5.2.2 Random Variable Generation 89\u003c\/p\u003e \u003cp\u003e5.3 Random Process Generation 93\u003c\/p\u003e \u003cp\u003e5.3.1 Markov Chains 93\u003c\/p\u003e \u003cp\u003e5.3.2 Markov Jump Processes 94\u003c\/p\u003e \u003cp\u003e5.4 Markov Chain Monte Carlo (MCMC) 97\u003c\/p\u003e \u003cp\u003e5.4.1 Metropolis-Hastings (M-H) Algorithm 97\u003c\/p\u003e \u003cp\u003e5.4.2 Gibbs Sampler 98\u003c\/p\u003e \u003cp\u003e5.4.3 Multiple-try Metropolis-Hastings (M-H) Method 99\u003c\/p\u003e \u003cp\u003e5.5 Rare-Event Simulation 101\u003c\/p\u003e \u003cp\u003e5.5.1 Importance Sampling 101\u003c\/p\u003e \u003cp\u003e5.5.2 Repetitive Simulation Trials after Reaching Thresholds (RESTART) 102\u003c\/p\u003e \u003cp\u003e5.6 Exercises 103\u003c\/p\u003e \u003cp\u003eAppendix 104\u003c\/p\u003e \u003cp\u003eReferences 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Uncertainty Treatment under Imprecise or Incomplete Knowledge 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Interval Number and Interval of Confidence 117\u003c\/p\u003e \u003cp\u003e6.1.1 Definition and Basic Arithmetic Operations 117\u003c\/p\u003e \u003cp\u003e6.1.2 Algebraic Properties 118\u003c\/p\u003e \u003cp\u003e6.1.3 Order Relations 119\u003c\/p\u003e \u003cp\u003e6.1.4 Interval Functions 120\u003c\/p\u003e \u003cp\u003e6.1.5 Interval of Confidence 121\u003c\/p\u003e \u003cp\u003e6.2 Fuzzy Number 121\u003c\/p\u003e \u003cp\u003e6.3 Possibility Theory 123\u003c\/p\u003e \u003cp\u003e6.3.1 Possibility Propagation 124\u003c\/p\u003e \u003cp\u003e6.4 Evidence Theory 125\u003c\/p\u003e \u003cp\u003e6.4.1 Data Fusion 128\u003c\/p\u003e \u003cp\u003e6.5 Random-fuzzy Numbers (RFNs) 128\u003c\/p\u003e \u003cp\u003e6.5.1 Universal Generating Function (UGF) Representation of Random-fuzzy Numbers 129\u003c\/p\u003e \u003cp\u003e6.5.2 Hybrid UGF (HUGF) Composition Operator 130\u003c\/p\u003e \u003cp\u003e6.6 Exercises 132\u003c\/p\u003e \u003cp\u003eReferences 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Applications 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Distributed Power Generation System Reliability Assessment 135\u003c\/p\u003e \u003cp\u003e7.1.1 Reliability of Power Distributed Generation (DG) System 135\u003c\/p\u003e \u003cp\u003e7.1.2 Energy Source Models and Uncertainties 136\u003c\/p\u003e \u003cp\u003e7.1.3 Algorithm for the Joint Propagation of Probabilistic and Possibilistic Uncertainties 138\u003c\/p\u003e \u003cp\u003e7.1.4 Case Study 140\u003c\/p\u003e \u003cp\u003e7.2 Nuclear Power Plant Components Degradation 140\u003c\/p\u003e \u003cp\u003e7.2.1 Dissimilar Metal Weld Degradation 140\u003c\/p\u003e \u003cp\u003e7.2.2 MCS Method 145\u003c\/p\u003e \u003cp\u003e7.2.3 Numerical Results 147\u003c\/p\u003e \u003cp\u003eReferences 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Optimization Methods and Applications 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Mathematical Programming 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Linear Programming (LP) 153\u003c\/p\u003e \u003cp\u003e8.1.1 Standard Form and Duality 155\u003c\/p\u003e \u003cp\u003e8.2 Integer Programming (IP) 159\u003c\/p\u003e \u003cp\u003e8.3 Exercises 164\u003c\/p\u003e \u003cp\u003eReferences 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Evolutionary Algorithms (EAs) 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Evolutionary Search 168\u003c\/p\u003e \u003cp\u003e9.2 Genetic Algorithm (GA) 170\u003c\/p\u003e \u003cp\u003e9.2.1 Encoding and Initialization 171\u003c\/p\u003e \u003cp\u003e9.2.2 Evaluation 172\u003c\/p\u003e \u003cp\u003e9.2.3 Selection 173\u003c\/p\u003e \u003cp\u003e9.2.4 Mutation 174\u003c\/p\u003e \u003cp\u003e9.2.5 Crossover 175\u003c\/p\u003e \u003cp\u003e9.2.6 Elitism 178\u003c\/p\u003e \u003cp\u003e9.2.7 Termination Condition and Convergence 178\u003c\/p\u003e \u003cp\u003e9.3 Other Popular EAs 179\u003c\/p\u003e \u003cp\u003e9.4 Exercises 181\u003c\/p\u003e \u003cp\u003eReferences 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multi-Objective Optimization (MOO) 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Multi-objective Problem Formulation 185\u003c\/p\u003e \u003cp\u003e10.2 MOO-to-SOO Problem Conversion Methods 187\u003c\/p\u003e \u003cp\u003e10.2.1 Weighted-sum Approach 188\u003c\/p\u003e \u003cp\u003e10.2.2 ε-constraint Approach 189\u003c\/p\u003e \u003cp\u003e10.3 Multi-objective Evolutionary Algorithms 190\u003c\/p\u003e \u003cp\u003e10.3.1 Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) 190\u003c\/p\u003e \u003cp\u003e10.3.2 Improved Strength Pareto Evolutionary Algorithm (SPEA 2) 193\u003c\/p\u003e \u003cp\u003e10.4 Performance Measures 197\u003c\/p\u003e \u003cp\u003e10.5 Selection of Preferred Solutions 200\u003c\/p\u003e \u003cp\u003e10.5.1 “Min-Max” Method 200\u003c\/p\u003e \u003cp\u003e10.5.2 Compromise Programming Approach 201\u003c\/p\u003e \u003cp\u003e10.6 Guidelines for Solving RAMS+C Optimization Problems 201\u003c\/p\u003e \u003cp\u003e10.7 Exercises 203\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Optimization under Uncertainty 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Stochastic Programming (SP) 207\u003c\/p\u003e \u003cp\u003e11.1.1 Two-stage Stochastic Linear Programs with Fixed Recourse 209\u003c\/p\u003e \u003cp\u003e11.1.2 Multi-stage Stochastic Programs with Recourse 217\u003c\/p\u003e \u003cp\u003e11.2 Chance-Constrained Programming 218\u003c\/p\u003e \u003cp\u003e11.2.1 Model and Properties 219\u003c\/p\u003e \u003cp\u003e11.2.2 Example 221\u003c\/p\u003e \u003cp\u003e11.3 Robust Optimization (RO) 222\u003c\/p\u003e \u003cp\u003e11.3.1 Uncertain Linear Optimization (LO) and its Robust Counterparts 223\u003c\/p\u003e \u003cp\u003e11.3.2 Tractability of Robust Counterparts 224\u003c\/p\u003e \u003cp\u003e11.3.3 Robust Optimization (RO) with Cardinality Constrained Uncertainty Set 225\u003c\/p\u003e \u003cp\u003e11.3.4 Example 226\u003c\/p\u003e \u003cp\u003e11.4 Exercises 228\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Applications 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Multi-objective Optimization (MOO) Framework for the Integration of Distributed Renewable Generation and Storage 231\u003c\/p\u003e \u003cp\u003e12.1.1 Description of Distributed Generation (DG) System 232\u003c\/p\u003e \u003cp\u003e12.1.2 Optimal Power Flow (OPF) 234\u003c\/p\u003e \u003cp\u003e12.1.3 Performance Indicators 235\u003c\/p\u003e \u003cp\u003e12.1.4 MOO Problem Formulation 237\u003c\/p\u003e \u003cp\u003e12.1.5 Solution Approach and Case Study Results 238\u003c\/p\u003e \u003cp\u003e12.2 Redundancy Allocation for Binary-State Series-Parallel Systems (BSSPSs) under Epistemic Uncertainty 240\u003c\/p\u003e \u003cp\u003e12.2.1 Problem Description 240\u003c\/p\u003e \u003cp\u003e12.2.2 Robust Model 241\u003c\/p\u003e \u003cp\u003e12.2.3 Experiment 243\u003c\/p\u003e \u003cp\u003eReferences 244\u003c\/p\u003e \u003cp\u003eIndex 245\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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