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Probabilistic Physics of Failure Approach to Reliability

Modeling, Accelerated Testing, Prognosis and Reliability Assessment
By Mohammad Modarres, Mehdi Amiri and Christopher Jackson
Series: Performability Engineering Series
Copyright: 2017   |   Status: Published
ISBN: 9781119388630  |  Hardcover  |  
282 pages | 118 illustrations
Price: $195 USD
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One Line Description
This book provides a comprehensive coverage of the salient features of the role probability and statistics play in predicting the uncertainty in the estimates of life and reliability.

The book is essential to researchers and engineers in reliability engineering, mechanical engineering, probabilistic risk assessment, materials degradation and failure, applied probability and statistics, fracture mechanics, prognosis and health management and applied physics.

Engineers are often involved with product development that are constantly challenged to reduce time-to-market, minimize warranty costs, and increase quality. The physics-of-failure approach and modeling through the use of probabilistic physics of failure (PPoF) is the modern approach to help engineers toward this end.
The book presents highly technical approaches to the probabilistic physics of failure analysis and applications to accelerated life and degradation testing to reliability prediction and assessment. Besides reviewing a select set of important failure mechanisms, the book covers basic and advanced methods of performing accelerated life test (ALT) and accelerated degradation tests, as well as analyzing the test data such as accelerated degradation testing, Highly Accelerated Life Testing (HALT), and Highly Accelerated Stress Screening (HASS).
The book includes a large number of very useful examples to help readers understand the complicated methods described. Finally, MATLAB, R and OpenBUGS computer scripts are provided and discussed to support complex computational probabilistic analyses introduced.

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Author / Editor Details
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 emphasis 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 modeling.

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Table of Contents
1 Overview of Probabilistic Physics-of-Failure Approach to Reliability
1.1 Introduction
1.2 Overview of Physics-of-Failure Modeling
1.3 Important Forms of PoF Models
1.4 PPoF Approach to Life Assessment
1.5 Accelerated Testing in PPoF Model Development
1.6 Organization of the Book
2 Summary of Mechanisms of Failure and Associated PoF Models
2.1 Introduction
2.2 Fatigue
2.2.1 Life-Stress The S-N Diagram Mean Stress Effects Combined Loading
2.2.2 Strain-Life Monotonic Stress-Strain Behavior Cyclic Stress-Strain Behavior Strain-Life Relationship Mean Stress Effects
2.2.3 Variable Amplitude Loading Non-Linear Damage Models
2.2.4 Notch Effect Life-Stress Strain-Life
2.2.5 Two-Stage Approach to Fatigue Life Estimation
2.2.6 Fracture Mechanics Stress Intensity Factor Region I Region II Region III Fracture Mechanics Approach with Notch Effect
2.2.7 Factors Infl uencing Fatigue Failure Size Effect Frequency Effect Environmental and External Effects Miscellaneous Factors
2.3 Wear
2.3.1 General Form of Wear Equations
2.3.2 Sliding Wear
2.3.3 Abrasive Wear
2.3.4 Impact Wear
2.3.5 Rolling Wear
2.3.6 Life Models for Bearings
2.3.7 Life Models for Seals
2.3.8 Wear of Lubricated Contacts
2.3.9 Lubricated Wear and Lubricant Life
2.4 Creep
2.4.1 Larson-Miller Theory
2.4.2 Manson-Haferd Theory
2.4.3 Creep Under Uniaxial State of Stress
2.4.4 Cumulative Creep Prediction
2.5 Corrosion
2.5.1 Models for Prediction of Corrosion Rate and Service Life
3 Types of Accelerated Testing and Modeling Concepts
3.1 Introduction
3.2 Types of Accelerated Testing – Qualitative and Quantitative
3.3 Qualitative Accelerated Tests
3.3.1 Environmental Stress Testing
3.3.2 Burn-In Testing
3.3.3 Environmental Stress Screening
3.3.4 Highly Accelerated Life Testing Summary of HALT Process
3.3.5 Highly Accelerated Stress Screening
3.4 Quantitative Accelerated Tests
3.4.1 Modeling Degradation Associated with Various Failure Mechanisms Stress-Strength Model Damage-Endurance Model Performance-Requirement Model
3.4.2 Forms of Degradation and Performance Models
4 Analysis of Accelerated Life Testing Data and Physics-Based Reliability Model Development
4.1 Introduction
4.2 Accelerated Life Data Analysis Methods
4.3 Basics of ALT Data Analysis
4.4 Types of Collected Accelerated Life Test Data
4.5 Life-stress Models
4.6 Probability Plotting Method for ALT Model Estimation
4.6.1 Life-Stress Model by Regression
4.6.2 Summary of Plotting Method for Analyzing ALT Data
4.7 Maximum Likelihood Estimation Approach to ALT Data Analysis
4.8 Confidence Intervals for MLE
4.9 MLE Approach to Estimating Parameters of Common Distributions
4.9.1 Exponential Life Distribution
4.9.2 Weibull Life Distribution
4.9.3 Lognormal Life Distribution
4.10 MLE-Based Parameter Estimation for Different Life-Stress Models
4.10.1 The Exponential Life-Stress Model
4.10.2 Exponential Life-Stress Model with Weibull Life Distribution
4.10.3 Exponential Life-Stress Model with Lognormal Life Distribution
4.10.4 The Eyring Life-Stress Model
4.10.5 The Eyring-Weibull Model
4.10.6 The Eyring-Lognormal Model
4.10.7 Power Life-Stress Model
4.10.8 Power Life-Stress with Weibull Life Model
4.10.9 Power Life-Stress with Lognormal Model
4.10.10 Dual-Stress Exponential Life-Stress Model
4.10.11 Dual-Stress Exponential Life-Stress Model with Weibull Life Distribution
4.10.12 Dual-Stress Exponential Life-Stress Model with Lognormal Life Distribution
4.10.13 Power-Exponential Life-Stress Model
4.10.14 Power-Exponential Life-Stress Model Weibull Life Distribution
4.10.15 Power-Exponential Life-Stress Model Lognormal Life Distribution
4.11 Proportional Hazards (PH) Model
4.11.1 The Parametric PH Model, with an Example
4.12 Bayesian Estimation Approach to ALT Model Parameter Estimation
4.12.1 Prior Information for Bayesian Estimation
4.12.2 A Bayesian Estimation ALT Data Analysis Example
4.13 Determining Stress Dependencies
4.13.1 Confidence Bounds
4.14 Summary of the ALT Steps and Common Problems in Practice
4.15 Time Varying Stress Tests
4.16 Step-Stress Analysis and Model Development
4.16.1 Plotting Method for Step-Stress Data Analysis
4.16.2 Maximum Likelihood Estimation Method for Step-Stress Data Analysis
4.16.3 Bayesian Inference Method for Step-Stress Data Analysis
5 Analysis of Accelerated Degradation Data and Reliability Model Development
5.1 Introduction
5.2 Degradation Models
5.2.1 Simple Degradation Model Without Variation
5.2.2 Consideration of the Variation in Degradation Model and Failure Time
5.2.3 General Degradation Path Model
5.2.4 Approximate Accelerated Life Degradation Analysis
5.2.5 Maximum Likelihood Approach to Estimating Acceleration Degradation Model Parameters
5.2.6 Bayesian Estimation of ADT Model Parameters
B>6 Accelerated Test Planning
6.1 Introduction
6.2 Issues to Consider Prior to Accelerated Testing
6.3 Planning for Accelerated Life Tests
6.3.1 Steps for Accelerated Life Tests
6.3.2 Optimal Design of Accelerated Life Test
6.4 Planning for Accelerated Degradation Tests
7 Accounting for Uncertainties and Model Validation
7.1 Introduction
7.2 Uncertainties in Evidence
7.2.1 Classical Error: Uncertainty in the Physical Process
7.2.2 Berkson Error: Uncertainty in the Observation Process Systematic Uncertainties Stochastic Uncertainties Relationship between Berkson and Classical Errors
7.3 PPoF Model Uncertainties, Errors, and Validation
7.4 Applications of Model Validation in ADT

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