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.
