A general Bayes Weibull inference model for accelerated life testing

摘要

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 Markov Chain Monte Carlo (MCMC) methods to derive posterior approximations. The approach is illustrated with an example.