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Author:   James E. Breneman (Pratt & Whitney, Division of Raytheon Technologies, USA) ,  Chittaranjan Sahay (University of Hartford, USA) ,  Elmer E. Lewis (Northwestern University, USA)
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Edition:   3rd edition
Dimensions:   Width: 18.00cm , Height: 2.50cm , Length: 25.90cm
Weight:   1.066kg
ISBN:  

9781119640561

ISBN 10:   1119640563
Pages:   640
Publication Date:   22 April 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

1 INTRODUCTION   1.1 Reliability Defined 1.2 Performance, Cost and Reliability 1.3 Quality, Reliability and Safety Linkage 1.4 Quality, Reliability and Safety Engineering Tasks 1.5 Preview 2 PROBABILITY AND DISCRETE DISTRIBUTIONS 2.1 Introduction 2.2 Probability Concepts Sample Space Outcome Event Probability Axioms More than two events Combinations and Permutations 2.3 Discrete Random Variables Properties of Discrete Variables The Binomial Distribution The Poisson Distribution Confidence Intervals Motivation for Confidence Intervals Introduction to Confidence Intervals Binomial Confidence Intervals Cumulative sums of the Poisson Distribution (Thorndike Chart) 3 Exponential Distribution and Reliability Basics 3.1 Introduction 3.2 Reliability Characterization Basic definitions The Bathtub curve 3.3 Constant Failure Rate model The Exponential Distribution Demand failures Time determinations 3.4 Time Dependent Failure rates 3.5 Component Failures and Failure Modes Failure mode rates Component counts 3.6 Replacements 3.7 Redundancy Active and Standby Redundancy Active Parallel Standby Parallel Constant Failure Rate Models 3.8 Redundancy limitations Common-mode failures Load sharing Switching & Standby failures Cool, Warm and Hot Standby 3.9 Multiply Redundant Systems 1/N Active Redundancy 1/N Standby Redundancy m/N Active Redundancy 3.10 Redundancy Allocation High and Low level redundancy Fail-safe and Fail-to-Danger Voting Systems 3.11 Redundancy in Complex Configurations Serial-Parallel configurations Linked configurations 4 Continuous Distributions- Part 1 Normal & Related Distributions 4.1 Introduction 4.2 Properties of Continuous Random variables Probability Distribution Functions Characteristics of a Probability Distribution Sample Statistics Transformation of Variables 4.3 Empirical Cumulative Distribution Function 4.4 Uniform Distribution 4.5 Normal and Related Distributions The Normal Distribution Central Limit Theorem The Central Limit Theorem in Practice The Log Normal Distribution Log Normal Distribution from a Physics of Failure Perspective 4.6 Confidence Intervals Point & Interval Estimates Estimate of the Mean Normal & Lognormal parameters 5 Continuous Distributions- Part 2 Weibull & Extreme Value Distributions 5.1 Introduction The “weakest link” theory from a Physics of Failure point of view Uses of Weibull and Extreme Value Distributions Other Considerations Age parameters and sample sizes Engineering Changes, Maintenance Plan Evaluation and Risk Prediction Weibulls with cusps or curves System Weibulls No failure Weibulls Small sample Weibulls                   5.2 Statistics of the Weibull Distribution Weibull “Mathematics” The Weibull Probability Plot Probability Plotting Points—Median Ranks How to do a “Weibull Analysis” Weibull plots and their estimates of b, h The 3-Parameter Weibull didn’t work, what are my choices? The data has a “dogleg” bend or cusp when plotted on Weibull paper. Steep Weibull slopes (β’s)  may hide problems. Low Time Failures and close Serial numbers---Batch problems Maximum Likelihood Estimates of β and η Weibayes Analysis Weibayes background Weibull Analysis with failure times only and unknown times on remaining population Shifting Weibull Procedure Confidence bounds and the Weibull Distribution Arbitrary Censored Data The Weibull Distribution in a System of Independent failure modes 5.3 Extreme Value Distributions Smallest & Largest Extreme Value distributions Extreme Value and Weibull Distribution Point Estimates & Confidence Intervals 5.4 Introduction to Risk analysis Risk Analysis “Mathematics” Supplement 1- Weibull derived from weakest link theory Supplement 2: Comparing two distributions using Supersmith™ 6 RELIABILITY TESTING 6.1 Introduction 6.2 Attribute Testing (Binomial Testing) The Classical Success Run Zero Failure Attribute Tests Non-ZERO Failure Attribute Tests 6.3 Constant Failure Rate Estimates Censoring on the Right MTTF Estimates Confidence Intervals 6.4 Weibull Substantiation and Reliability Testing Zero-Failure Test Plans for Substantiation Testing Weibull Zero-Failure test Plans for Reliability Testing Designing the Test Plan Total Test Time Why not Simply Test to Failure? 6.5 How to Reduce Test Time Run (simultaneously) more test samples than you intend to fail Sudden Death Testing Sequential Testing 6.6 Normal & Lognormal Reliability Testing 6.7 Accelerated Life Testing Compressed Time Testing Advanced Stress Testing-Linear & Acceleration Models Linear Model Stress testing Advanced Stress Testing – Acceleration Models The Arrhenius Model The Inverse Power Law Model Other Acceleration Models 6.8 Reliability Enhancement Procedures Reliability Growth Modeling & Testing Calculation of Reliability Growth parameters Goodness of Fit tests for Reliability Growth Models Environmental Stress Screening What “Screens” are used for ESS? Thermal cycling Random Vibration Other Screens Highly Accelerated Life Tests Highly Accelerated Stress Screening Supplement 1 Substantiation Testing: Characteristic Life multipliers for Zero failure Test  at 80%, 90%, 95%, 99% Confidence Supplement 2 Substantiation Testing Tables for Zero failure Test  at 80%, 90%, 95%, 99% Confidence Supplement 3 CRITICAL VALUES FOR CRAMER-VON MISES GOODNESS-OF-FIT TEST Supplement 4 Other Reliability Growth Models Supplement 5 Chi-Square Table 7 Failure Modes & Effects Analysis (FMEA) – Design & Process 7.1 Introduction 7.2 Functional FMEA 7.3 Design FMEA Design FMEA Procedure 7.4 Process FMEA(PFMEA) 7.5 FMEA Summary FMEA Outputs FMEA Pitfalls that can be prevented Supplement 1 Shortcut tables for stalled FMEA Teams Supplement 2 Future changes in FMEA Approaches Supplement 3 DFMEA and PFMEA Forms 8 LOADS, CAPACITY, AND RELIABILITY 8.1 Introduction 8.2 Reliability with a Single Loading Load Application Definitions 8.3 Reliability and Safety Factors Normal Distributions Lognormal Distributions Combined Distributions 8.4 Repetitive Loading Loading Variability Variable Capacity 8.5 The Bathtub Curve—Reconsidered Single Failure Modes Combined Failure Modes Supplement 1: The Dirac Delta Distribution 9 MAINTAINED SYSTEMS 9.1 Introduction 9.2 Preventive Maintenance Idealized Maintenance Imperfect Maintenance Redundant Components 9.3 Corrective Maintenance Availability Maintainability 9.4 Repair: Revealed Failures Constant Repair Rates Constant Repair Times 9.5 Testing and Repair: Unrevealed Failures Idealized Periodic Tests Real Periodic Tests 9.6 System Availability Revealed Failures Unrevealed Failures 10 FAILURE INTERACTIONS 10.1 Introduction 10.2 Markov Analysis  Two Independent Components  Load-Sharing Systems 10.3 Reliability with Standby Systems Idealized System Failures in the Standby State Switching Failures Primary System Repair 10.4 Multicomponent Systems Multicomponent Markov Formulations Combinations of Subsystems 10.5 Availability Standby Redundancy Shared Repair Crews Markov Availability-Advantages & Disadvantages 11 SYSTEM SAFETY ANALYSIS 11.1 Introduction 11.2 Product and Equipment Hazards 11.3 Human Error Routine Operations Emergency Operations 11.4 Methods of Analysis Failure Modes Effects and Criticality Analysis (FMECA) Event Trees 11.5 Fault Trees Fault-Tree Construction Nomenclature Fault Classification Fault Tree Examples Direct Evaluation of Fault Trees Qualitative Evaluation Quantitative Evaluation Fault-Tree Evaluation by Cut Sets Qualitative Analysis Quantitative Analysis 11.6 Reliability/Safety Risk Analysis APPENDICES A USEFUL MATHEMATICAL RELATIONSHIPS B BINOMIAL CONFIDENCE CHARTS C STANDARD NORMAL CDF D NONPARAMETRIC METHODS AND PROBABILITY PLOTTING D1 Introduction D2 Nonparametric Methods for Probability Plotting D3 Parametric Methods D4 Goodness-of-Fit         Supplement 1 Further Details of Weibull Probability plotting Supplement 2 Median Rank adjustment for SUSPENDED TEST ITEMS Supplement 3 Generating a Probability Plot in MINITAB ANSWERS TO ODD-NUMBERED EXERCISES INDEX

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James E. Breneman established and headed the Engineering Technical University at Pratt and Whitney, which provided more than 450,000 hours of instruction to employees during his tenure. Now retired, Breneman has taught many public course offerings for the ASQ Reliability & Risk Division. In 2018 he was awarded the Eugene L. Grant Medal for outstanding leadership in educational programs in quality. Chittaranjan Sahay holds the Vernon D. Roosa Distinguished Professor Chair in Manufacturing and Professorship in Mechanical Engineering at the University of Hartford, where he has held various offices including Associate Dean and Director of the Graduate Programs of the College of Engineering, Technology, and Architecture, and Chairman of the Mechanical Engineering Department. Elmer E. Lewis is Professor of Mechanical Engineering at Northwestern University’s McCormick School of Engineering and Applied Science. He has held appointments as Visiting Professor at the University of Stuttgart and as Guest Scientist at the Nuclear Research Center at Karlsruhe, Germany. He has been a frequent consultant to Argonne and Los Alamos National Laboratories as well as a number of industrial firms.

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