A general Bayes weibull inference model for accelerated life testing

Journal article (2005)

Authors

JR van Dorp OLD Operations Research and Risk Analysis
TA Mazzuchi OLD Operations Research and Risk Analysis
Research Group
OLD Operations Research and Risk Analysis
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Published Date

2005

Language

Undefined/Unknown

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Research Group

OLD Operations Research and Risk Analysis

Abstract

This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distribution for the scale parameters at the various stress levels and the common shape parameter. Using the approach, Bayes point estimates as well as probability statements for use-stress (and accelerated) life parameters may be inferred from a host of testing scenarios. The inference procedure accommodates both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The inference procedure uses the well-known MCMC (Markov Chain Monte Carlo) methods to derive posterior approximations. The approach is illustrated with an example.

Keywords: Dirichlet distribution; Environmental testing; Step-stress testing