Probabilistic Physics of Failure Approach to Reliability

Mohammad Modarres is Director, Center for Risk and Reliability and the Nicole Y. Kim Eminent Professor of Engineering, University of Maryland; M.S. (1977) and PhD (1980) in Nuclear Engineering from MIT, and M.S. in Mechanical Engineering also from MIT (1977). He has more than 400 papers in archival journals and proceedings of conferences, including several books and textbooks in various areas of nuclear safety, risk and reliability engineering. He is a University of Maryland Distinguished Scholar-Teacher and a fellow of the American Nuclear Society.Mehdi Amiri is an adjunct professor in the Department of Mechanical Engineering, George Mason University; M.S. (2006) in Mechanical Engineering from University of Tehran, Iran and PhD (2011) in Mechanical Engineering from Louisiana State University (LSU). His main research interests include materials, failure prediction through simulation-based modeling of the microstructural defects, with current emphases on additively manufactured materials, and nondestructive evaluation and testing.Christopher Jackson is the Acting Director of the Centre of Reliability and Resilience Engineering at the B. John Garrick Institute for the Risk Sciences, University of California, Los Angeles. He graduated with a PhD (2011) and MS (2007) in Reliability Engineering from the University of Maryland, a Masters of Military Studies (2012) from the Australian National University, and a Bachelor of Engineering (Mechanical 2001) from the University of New South Wales. His current research thrusts revolve around Bayesian analysis, big data analysis and complex system modelling.

• Preface xi1 Overview of Probabilistic Physics-of-Failure Approach to Reliability 11.1 Introduction 11.2 Overview of Physics-of-Failure Modeling 21.3 Important Forms of PoF Models 41.4 PPoF Approach to Life Assessment 61.5 Accelerated Testing in PPoF Model Development 81.6 Organization of the Book 10References 112 Summary of Mechanisms of Failure and Associated PoF Models 132.1 Introduction 132.2 Fatigue 152.3 Wear 602.4 Creep 812.5 Corrosion 90References 973 Types of Accelerated Testing and Modeling Concepts 1013.1 Introduction 1013.2 Types of Accelerated Testing – Qualitative and Quantitative 1013.3 Qualitative Accelerated Tests 1023.4 Quantitative Accelerated Tests 107References 1154 Analysis of Accelerated Life Testing Data and Physics-Based Reliability Model Development 1174.1 Introduction 1174.2 Accelerated Life Data Analysis Methods 1174.3 Basics of ALT Data Analysis 1174.4 Types of Collected Accelerated Life Test Data 1184.5 Life-stress Models 1194.6 Probability Plotting Method for ALT Model Estimation 1244.7 Maximum Likelihood Estimation Approach to ALT Data Analysis 1314.8 Confidence Intervals for MLE 1344.9 MLE Approach to Estimating Parameters of Common Distributions 1364.10 MLE-Based Parameter Estimation for Different Life-stress Models 1394.11 Proportional Hazards (PH) Model 1684.12 Bayesian Estimation Approach to ALT Model Parameter Estimation 1714.13 Determining stress dependencies 1754.14 Summary of the ALT Steps and Common Problems in Practice 1784.15 Time Varying Stress Tests 1794.16 Step-Stress Analysis And Model Development 182References 2015 Analysis of Accelerated Degradation Data and Reliability Model Development 2035.1 Introduction 2035.2 Degradation Models 205References  2316 Accelerated Test Planning 2336.1 Introduction 2336.2 Issues to Consider Prior to Accelerated Testing 2336.3 Planning for Accelerated Life Tests 2376.4 Planning for Accelerated Degradation Tests 246References 2507 Accounting for Uncertainties and Model Validation 2517.1 Introduction 2517.2 Uncertainties in Evidence 2517.3 PPoF Model Uncertainties, Errors, and Validation 2597.4 Applications of Model Validation in ADT 263References 268Index 269

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