Series Editor's Foreword xxiPreface xxiiiAcknowledgement xxvAbout the Companion Website xxvii1 Basic Reliability Concepts and Models 11.1 Introduction 11.2 Reliability Definition and Hazard Rate 11.3 Mean Lifetime and Mean Residual Life 91.4 System Downtime and Availability 141.5 Discrete Random Variable for Reliability Modeling 151.6 Continuous Random Variable for Reliability Modeling 181.7 Bayesian Reliability Model 281.8 Markov Model and Poisson Process 30References 34Problems 352 Reliability Estimation with Uncertainty 412.1 Introduction 412.2 Reliability Block Diagram 412.3 Series Systems 432.4 Parallel Systems 472.5 Mixed Series and Parallel Systems 492.6 Systems with k-out-of-n:G Redundancy 552.7 Network Systems 582.8 Reliability Confidence Intervals 662.9 Reliability of Multistate Systems 682.10 Reliability Importance 71References 78Problems 813 Design and Optimization for Reliability 893.1 Introduction 893.2 Lifecycle Reliability Optimization 893.3 Reliability and Redundancy Allocation 953.4 Multiobjective Reliability-Redundancy Allocation 1033.5 Failure-in-Time Based Design 1083.6 Failure Rate Considering Uncertainty 1153.7 Fault-Tree Method 1183.8 Failure Mode, Effect, and Criticality Analysis 1213.9 Case Study: Reliability Design for Six Sigma 123References 127Problems 1294 Reliability Growth Planning 1334.1 Introduction 1334.2 Classification of Failures 1334.3 Failure Mode Types 1364.4 No Fault Found (NFF) Failures 1384.5 Corrective Action Effectiveness 1414.6 Reliability Growth Model 1454.7 Reliability Growth and Demonstration Test 1544.8 Lifecycle Reliability Growth Planning 1594.9 Case Study 164References 166Problems 1695 Accelerated Stress Testing and Economics 1715.1 Introduction 1715.2 Design of Accelerated Stress Test 1715.3 Scale Acceleration Model and Usage Rate 1785.4 Arrhenius Model 1845.5 Eyring Model and Power Law Model 1875.6 Semiparametric Acceleration Models 1905.7 Highly Accelerated Stress Screening Testing 1955.8 A Case Study for HASS Project 199References 204Problems 2066 Renewal Theory and Superimposed Renewal 2116.1 Introduction 2116.2 Renewal Integral Equation 2116.3 Exponential and Erlang Renewal 2196.4 Generalized Exponential Renewal 2216.5 Weibull Renewal with Decreasing Failure Rate 2266.6 Weibull Renewal with Increasing Failure Rate 2306.7 Renewal under Deterministic Fleet Expansion 2396.8 Renewal under Stochastic Fleet Expansion 2456.9 Case Study 248References 252Problems 2557 Performance-Based Maintenance 2597.1 Introduction 2597.2 Corrective Maintenance 2597.3 Preventive Maintenance 2627.4 Condition-Based Maintenance 2677.5 Inverse Gaussian Degradation Process 2757.6 Non-Stationary Gaussian Degradation Process 2787.7 Performance-Based Maintenance 2857.8 Contracting for Performance-Based Logistics 2937.9 Case Study - RUL Prediction of Electronics Equipment 295Appendix 298References 299Problems 3048 Warranty Models and Services 3098.1 Introduction 3098.2 Warranty Concept and Its Roles 3098.3 Warranty Policy for Non-repairable Product 3128.4 Warranty Models for Repairable Products 3218.5 Warranty Service for Variable Installed Base 3258.6 Warranty Service under Reliability Growth 3298.7 Other Warranty Services 3358.8 Case Study: Design for Warranty 340References 343Problems 3469 Basic Spare Parts Inventory Models 3499.1 Introduction 3499.2 Overview of Inventory Model 3499.3 Deterministic EOQ Model 3529.4 The News vendor Model 3579.5 The (q, r) Inventory System under Continuous Review 3619.6 The (s, S, T) Policy under Periodic Review 3689.7 Basic Supply Chain Systems 3729.8 Spare Parts Demand Forecasting 377References 383Problems 38710 Repairable Inventory System 39110.1 Introduction 39110.2 Characteristics of Repairable Inventory Systems 39110.3 Single-Echelon Inventory with Uncapacitated Repair 39610.4 Single-Echelon Inventory with Capacitated Repair 40210.5 Repairable Inventory for a Finite Fleet Size 40510.6 Single-Echelon Inventory with Emergency Repair 40810.7 Repairable Inventory Planning under Fleet Expansion 41210.8 Multi-echelon, Multi-item Repairable Inventory 41710.9 Case Study: Teradyne's Spare Parts Supply Chain 424References 432Problems 43411 Reliability and Service Integration 43911.1 Introduction 43911.2 The Rise of Product-Service System 43911.3 Allocation of Reliability and Inventory for a Static Fleet 44411.4 Allocation of Reliability and Inventory under Fleet Expansion 45111.5 Joint Allocation of Maintenance, Inventory, and Repair 45811.6 Case Study: Supporting Wind Generation Using PBC 467Appendix 470References 475Problems 47912 Resilience Engineering and Management 48112.1 Introduction 48112.2 Resilience Concept and Measures 48112.3 Disaster Resilience Models of Power Grid 48912.4 Prevention, Survivability, and Recovery 50012.5 Variable Generation System Model 50812.6 Case Study: Design for Resilient Distribution Systems 512References 516Problems 520Index 525
Tongdan Jin, PhD, is an Associate Professor in the Ingram School of Engineering at Texas State University. He obtained his Ph.D. in Industrial and Systems Engineering, and MS in Electrical and Computer Engineering, both from Rutgers University. His BS in Electrical and Automation Engineering is from Shaanxi University of Science and Technology, Xian, China. Prior to his academic appointment, he has five-year reliability design and management experience in Teradyne Inc., Boston. He is a recipient of best papers in several international conferences, including Evans-McElroy best paper in 2014 Reliability and Maintainability Conference. He has authored and co-authored over 140 journal and conference papers in reliability modeling and optimization with applications in manufacturing and energy systems. His research has been sponsored by NSF, the US Department of Agriculture, and the US Department of Education. He is the member of IEEE, INFORMS and IISE.