A general Bayes weibull inference model for accelerated life testing

JR van Dorp, TA Mazzuchi

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    40 Citations (Scopus)

    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
    Original languageUndefined/Unknown
    Pages (from-to)140-147
    Number of pages8
    JournalReliability Engineering & System Safety
    Volume90
    Issue number2-3
    DOIs
    Publication statusPublished - 2005

    Keywords

    • academic journal papers
    • ZX CWTS 1.00 <= JFIS < 3.00

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