Accelerated Life Testing of One-Shot Devices - by Narayanaswamy Balakrishnan & Man Ho Ling & Hon Yiu So (Hardcover)
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<p/><br></br><p><b> About the Book </b></p></br></br>"A one-shot device is a unit that performs its function only once and cannot be used for testing more than once. Examples include electric explosive devices, fire extinguishers, airbags in cars, and missiles. While testing one-shot devices, only the condition of the device at a specific inspection time can be recorded, and exact failure times cannot be obtained from the test. As a result, the lifetimes of devices are either left- or right-censored. Due to a lack of lifetime data collected in life-tests, estimating the reliability of one-shot devices in traditional approaches becomes challenging. This book primarily focuses on fundamental issues of statistical modeling based on one-shot device testing data collected from accelerated life-tests. This book also provides advanced statistical techniques. For instance, expectation-maximization algorithms and Bayesian approaches to deal with the estimation challenges, along with comprehensive data analysis of one-shot devices under accelerated life-tests. Readers may apply the techniques from this book to their own lifetime data with censoring. This book is ideal for graduate students, researchers, and engineers working on accelerated life testing data analysis"--<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>Provides authoritative guidance on statistical analysis techniques and inferential methods for one-shot device life-testing</b></p> <p>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.</p> <p><i>Accelerated Life Testing of One-shot Devices: Data Collection and Analysis</i> 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: </p> <ul> <li>Provides expert guidance on comprehensive data analysis of one-shot devices under accelerated life-tests</li> <li>Discusses how to design experiments for data collection from efficient accelerated life-tests while conforming to budget constraints</li> <li>Helps readers develops optimal designs for constant-stress and step-stress accelerated life-tests, mainstream life-tests commonly used in reliability practice</li> <li>Includes R code in each chapter for readers to use in their own analyses of one-shot device testing data</li> <li>Features numerous case studies and practical examples throughout</li> <li>Highlights important issues, problems, and future research directions in reliability theory and practice</li> </ul> <i>Accelerated Life Testing of One-shot Devices: Data Collection and Analysis</i> is essential reading for graduate students, researchers, and engineers working on accelerated life testing data analysis.<p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b>PROVIDES AUTHORITATIVE GUIDANCE ON STATISTICAL ANALYSIS TECHNIQUES AND INFERENTIAL METHODS FOR ONE-SHOT DEVICE LIFE-TESTING</b> <p>Estimating the reliability of one-shot devices--electro-explosive devices, fire extinguishers, automobile airbags, and other units that perform their function only once--poses unique analytical challenges to conventional approaches. Due to the nature of data collection, the precise failure times of devices cannot be obtained from tests. The condition of a one-shot device can only be recorded at a specific inspection time, resulting in a lack of lifetime data collected from tests. <p><i>Accelerated Life Testing of One-Shot Devices: Data Collection and Analysis</i> 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: <li>Provides expert guidance on comprehensive data analysis of one-shot devices under accelerated life-tests</li> <li>Discusses how to design experiments for data collection from efficient accelerated life-tests while conforming to budget constraints</li> <li>Helps readers develop optimal designs for constant-stress and step-stress accelerated life-tests, mainstream life-tests commonly used in reliability practice</li> <li>Includes R code in each chapter for readers to use in their own analyses of one-shot device testing data</li> <li>Features numerous case studies and practical examples throughout</li> <li>Highlights important issues, problems, and future research directions in reliability theory and practice</li> <p><i>Accelerated Life Testing of One-Shot Devices</i> is essential reading for graduate students, researchers, and engineers working on accelerated life testing data analysis.<p/><br></br><p><b> About the Author </b></p></br></br><p><b>NARAYANASWAMY BALAKRISHNAN, PhD, </b> is Distinguished University Professor, Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.</p> <p><b>MAN HO LING, PhD, </b> is Associate Professor, Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China. <p><b>HON YIU SO</b> is Post-Doctoral Fellow, University of Waterloo, Waterloo, Ontario, Canada.
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