Accelerated Life Testing of One-shot Devices

Accelerated Life Testing of One-shot Devices
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Provides authoritative guidance on statistical analysis techniques and inferential methods for one-shot device life-testing Estimating the reliability of one-shot devices—electro-expolsive devices, fire extinguishers, automobile airbags, and other units that perform their function only once—poses unique analytical challenges to conventional approaches. Due to how one-shot devices are censored, their precise failure times cannot be obtained from testing. The condition of a one-shot device can only be recorded at a specific inspection time, resulting in a lack of lifetime data collected in life-tests. Accelerated Life Testing of One-shot Devices: Data Collection and Analysis addresses the fundamental issues of statistical modeling based on data collected from accelerated life-tests of one-shot devices. The authors provide inferential methods and procedures for planning accelerated life-tests, and describe advanced statistical techniques to help reliability practitioners overcome estimation problems in the real world. Topics covered include likelihood inference, competing-risks models, one-shot devices with dependent components, model selection, and more. Enabling readers to apply the techniques to their own lifetime data and arrive at the most accurate inference possible, this practical resource: Provides expert guidance on comprehensive data analysis of one-shot devices under accelerated life-tests Discusses how to design experiments for data collection from efficient accelerated life-tests while conforming to budget constraints Helps readers develops optimal designs for constant-stress and step-stress accelerated life-tests, mainstream life-tests commonly used in reliability practice Includes R code in each chapter for readers to use in their own analyses of one-shot device testing data Features numerous case studies and practical examples throughout Highlights important issues, problems, and future research directions in reliability theory and practice Accelerated Life Testing of One-shot Devices: Data Collection and Analysis is essential reading for graduate students, researchers, and engineers working on accelerated life testing data analysis.

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Narayanaswamy Balakrishnan. Accelerated Life Testing of One-shot Devices

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Accelerated Life Testing of One‐shot Devices:

Preface

About the Companion Website

1 One‐Shot Device Testing Data. 1.1 Brief Overview

1.2 One‐Shot Devices

1.3 Accelerated Life‐Tests

1.4 Examples in Reliability and Survival Studies. 1.4.1 Electro‐Explosive Devices Data

1.4.2 Glass Capacitors Data

1.4.3 Solder Joints Data

1.4.4 Grease‐Based Magnetorheological Fluids Data

1.4.5 Mice Tumor Toxicological Data

1.4.6 ED01 Experiment Data

1.4.7 Serial Sacrifice Data

1.5 Recent Developments in One‐Shot Device Testing Analysis

2 Likelihood Inference. 2.1 Brief Overview

2.2 Under CSALTs and Different Lifetime Distributions

2.3 EM‐Algorithm

2.3.1 Exponential Distribution

2.3.2 Gamma Distribution

2.3.3 Weibull Distribution

2.4 Interval Estimation

2.4.1 Asymptotic Confidence Intervals

2.4.2 Approximate Confidence Intervals

2.5 Simulation Studies

2.6 Case Studies with R Codes

3 Bayesian Inference. 3.1 Brief Overview

3.2 Bayesian Framework

3.3 Choice of Priors

3.3.1 Laplace Prior

3.3.2 Normal Prior

3.3.3 Beta Prior

3.4 Simulation Studies

3.5 Case Study with R Codes

4 Model Mis‐Specification Analysis and Model Selection. 4.1 Brief Overview

4.2 Model Mis‐Specification Analysis

4.3 Model Selection

4.3.1 Akaike Information Criterion

4.3.2 Bayesian Information Criterion

4.3.3 Distance‐Based Test Statistic

4.3.4 Parametric Bootstrap Procedure for Testing Goodness‐of‐Fit

4.4 Simulation Studies

4.5 Case Study with R Codes

5 Robust Inference. 5.1 Brief Overview

5.2 Weighted Minimum Density Power Divergence Estimators

5.3 Asymptotic Distributions

5.4 Robust Wald‐type Tests

5.5 Influence Function

5.6 Simulation Studies

5.7 Case Study with R Codes

6 Semi‐Parametric Models and Inference. 6.1 Brief Overview

6.2 Proportional Hazards Models

6.3 Likelihood Inference

6.4 Test of Proportional Hazard Rates

6.5 Simulation Studies

6.6 Case Studies with R Codes

7 Optimal Design of Tests. 7.1 Brief Overview

7.2 Optimal Design of CSALTs

7.3 Optimal Design with Budget Constraints

7.3.1 Subject to Specified Budget and Termination Time

7.3.2 Subject to Standard Deviation and Termination Time

7.4 Case Studies with R Codes

7.5 Sensitivity of Optimal Designs

8 Design of Simple Step‐Stress Accelerated Life‐Tests. 8.1 Brief Overview

8.2 One‐Shot Device Testing Data Under Simple SSALTs

8.3 Asymptotic Variance. 8.3.1 Exponential Distribution

8.3.2 Weibull Distribution

8.3.3 With a Known Shape Parameter

8.3.4 With a Known Parameter About Stress Level

8.4 Optimal Design of Simple SSALT

8.5 Case Studies with R Codes. 8.5.1 SSALT for Exponential Distribution

8.5.2 SSALT for Weibull Distribution

9 Competing‐Risks Models. 9.1 Brief Overview

9.2 One‐Shot Device Testing Data with Competing Risks

9.3 Likelihood Estimation for Exponential Distribution

9.3.1 Without Masked Failure Modes

9.3.2 With Masked Failure Modes

9.4 Likelihood Estimation for Weibull Distribution

9.5 Bayesian Estimation

9.5.1 Without Masked Failure Modes

9.5.2 Laplace Prior

9.5.3 Normal Prior

9.5.4 Dirichlet Prior

9.5.5 With Masked Failure Modes

9.6 Simulation Studies

9.7 Case Study with R Codes

10 One‐Shot Devices with Dependent Components. 10.1 Brief Overview

10.2 Test Data with Dependent Components

10.3 Copula Models

10.3.1 Family of Archimedean Copulas

10.3.2 Gumbel–Hougaard Copula

10.3.3 Frank Copula

10.4 Estimation of Dependence

10.5 Simulation Studies

10.6 Case Study with R Codes

11 Conclusions and Future Directions. 11.1 Brief Overview

11.2 Concluding Remarks. 11.2.1 Large Sample Sizes for Flexible Models

11.2.2 Accurate Estimation

11.2.3 Good Designs Before Data Analysis

11.3 Future Directions. 11.3.1 Weibull Lifetime Distribution with Threshold Parameter

11.3.2 Frailty Models

11.3.3 Optimal Design of SSALTs with Multiple Stress Levels

11.3.4 Comparison of CSALTs and SSALTs

Appendix A Derivation of

Appendix B Observed Information Matrix

Appendix C Non‐Identifiable Parameters for SSALTs Under Weibull Distribution

Appendix D Optimal Design Under Weibull Distributions with Fixed

Appendix E Conditional Expectations for Competing Risks Model Under Exponential Distribution

Appendix F Kendall's Tau for Frank Copula

Bibliography

Author Index

Index

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Data Collection And Analysis

Narayanaswamy Balakrishnan

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Table 1.3 Failure records on solder joints under CSALTs with temperature (K) and a dichotomous variable indicating if the PCB type is “copper‐nickel‐tin (CNT)” or not.

Source: Lau et al. (1988).

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