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OPTIMAL DESIGN OF SIMPLE STEP-STRESS ACCELERATED LIFE TESTS FOR ONE-SHOT DEVICES UNDER EXPONENTIAL DISTRIBUTIONS

Published online by Cambridge University Press:  12 February 2018

Man Ho Ling Open the ORCID record for Man Ho Ling [Opens in a new window]
Man Ho Ling*
Affiliation:
Department of Mathematics and Information Technology, The Education University of Hong Kong, Tai Po, Hong Kong SAR, People's Republic of China E-mail: amhling@eduhk.hk
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Abstract

This paper considers simple step-stress accelerated life tests (SSALTs) for one-shot devices. The one-shot device is an item that cannot be used again after the test, for instance, munitions, rockets, and automobile air-bags. Either left-or right-censored data are collected instead of actual lifetimes of the devices under test. An expectation-maximization algorithm is developed here to find the maximum likelihood estimates of the model parameters based on one-shot device testing data collected from simple SSALTs. Furthermore, the asymptotic variance of the mean lifetime under normal operating conditions is determined under the expectation-maximization framework. On the other hand, the optimal design that minimizes the asymptotic variance of the estimate of the mean lifetime under normal operating conditions in terms of three decision variables, including stress levels, inspection times, and sample allocation is discussed. A procedure then is presented to determine the decision variables when a range of stress levels and the termination time of the test as well as normal operating conditions of the devices are given. The properties of the optimal design and the effects of errors in pre-specified planning values of the model parameters are also investigated. Comprehensive simulation studies show that the procedure is quite reliable for the design of simple SSALTs.

Keywords

cumulative exposure modelEM algorithmexponential distributionone-shot devicesoptimal designstep-stress accelerated life-tests
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 33 , Issue 1 , January 2019 , pp. 121 - 135
Copyright
Copyright © Cambridge University Press 2018 

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