Bayesian Analysis for Accelerated Life Tests Using a Dirichlet Process Weibull Mixture Model

This study proposes a semiparametric Bayesian approach to accelerated life test (ALT). The proposed accelerated life test model assumes a log-linear lifetime-stress relationship, without making any assumption on the parametric form of the failure-time distribution. A Dirichlet process mixture model with a Weibull kernel is employed to model the failure-time distribution at a given stress level. A simulation-based model fitting algorithm that implements Gibbs sampling is developed to analyze right-censored ALT data, and to predict the failure-time distribution at the normal stress level. The proposed model and algorithm are applied to two practical examples related to the reliability of nanoelectronic devices. The results have demonstrated that the proposed methodology is capable of providing accurate prediction of the failure-time distribution at the normal stress level without assuming any restrictive parametric failure-time distribution.