Accelerated Life Testing of One-Shot Devices

Data Collection and Analysis
1. Auflage 2021
ISBN: 978-1-119-66400-0
Verlag: Wiley

Buch, Englisch, 240 Seiten, Format (B × H): 157 mm x 235 mm, Gewicht: 503 g

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.

Autoren/Hrsg.

Balakrishnan, Narayanaswamy

Ling, Man Ho

So, Hon Yiu

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Industrielle Qualitätskontrolle
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Zuverlässigkeitstechnik

Inhaltsverzeichnis

Preface xi

About the Companion Website xiii

1 One-Shot Device Testing Data 1

1.1 Brief Overview 1

1.2 One-Shot Devices 1

1.3 Accelerated Life-Tests 3

1.4 Examples in Reliability and Survival Studies 4

1.4.1 Electro-Explosive Devices Data 4

1.4.2 Glass Capacitors Data 5

1.4.3 Solder Joints Data 5

1.4.4 Grease-Based Magnetorheological Fluids Data 6

1.4.5 Mice Tumor Toxicological Data 7

1.4.6 ED01 Experiment Data 7

1.4.7 Serial Sacrifice Data 7

1.5 Recent Developments in One-Shot Device Testing Analysis 10

2 Likelihood Inference 13

2.1 Brief Overview 13

2.2 Under CSALTs and Different Lifetime Distributions 13

2.3 EM-Algorithm 14

2.3.1 Exponential Distribution 16

2.3.2 Gamma Distribution 18

2.3.3 Weibull Distribution 21

2.4 Interval Estimation 26

2.4.1 Asymptotic Confidence Intervals 26

2.4.2 Approximate Confidence Intervals 28

2.5 Simulation Studies 30

2.6 Case Studies with R Codes 41

3 Bayesian Inference 47

3.1 Brief Overview 47

3.2 Bayesian Framework 47

3.3 Choice of Priors 49

3.3.1 Laplace Prior 49

3.3.2 Normal Prior 49

3.3.3 Beta Prior 50

3.4 Simulation Studies 51

3.5 Case Study with R Codes 59

4 Model Mis-Specification Analysis and Model Selection 65

4.1 Brief Overview 65

4.2 Model Mis-Specification Analysis 65

4.3 Model Selection 66

4.3.1 Akaike Information Criterion 66

4.3.2 Bayesian Information Criterion 67

4.3.3 Distance-Based Test Statistic 68

4.3.4 Parametric Bootstrap Procedure for Testing Goodness-of-Fit 70

4.4 Simulation Studies 70

4.5 Case Study with R Codes 76

5 Robust Inference 79

5.1 Brief Overview 79

5.2 Weighted Minimum Density Power Divergence Estimators 79

5.3 Asymptotic Distributions

Über Autor(innen)

NARAYANASWAMY BALAKRISHNAN, PhD, is Distinguished University Professor, Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.

MAN HO LING, PhD, is Associate Professor, Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China.

HON YIU SO is Post-Doctoral Fellow, University of Waterloo, Waterloo, Ontario, Canada.