ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Bayesian Adaptive Planning in Type II Step-Stress Accelerated Life Tests with Multiple Steps

12 September 2017, 10:10 – 10:30


Submitted by
Seiichi Yasui
Masakatsu Onoda (Tokyo University of Science), Seiichi Yasui (Tokyo University of Science)
One of the accelerated life tests to economically estimate the lifetime distribution of the product is a step-stress test in which the stress levels are changed in stages within the test. In general, the stress levels are monotonically increased or decreased, and the amount of stress for each step and timing of the change are predetermined before the test. Therefore, the test plan needs to be set up with information on the test items. Above all it is important to set appropriate levels of stress for each step. However, if the information on the test items is insufficient, many test items may instantly fail when raising the stress level, or no failure is observed in the step at all, which leads to poor estimation accuracy.
We propose type II step-stress accelerated life tests with multiple steps, in which the stress level for each step is adaptively determined by Bayesian estimation of the lifetime distribution based on failure times previously obtained during the life test. The adaptive determination of stress levels makes it possible to control the probability that some items to be tested in a step fail within minimum unit of observational time after changing stress levels.
In this paper, it is assumed that lifetimes are distributed according to the Weibull distribution. We deal with some asymmetric absolute loss functions in Bayesian decision making to control the probability of multiple failures at changing stress levels. The estimation accuracy of parameters in the inverse power law is evaluated through Monte Carlo methods.
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