A Full Bayesian Approach for Masked Data in Step-Stress Accelerated Life Testing

Abstract

Bayesian analysis of the series system failure data under step-stress accelerating life testing is proposed when the cause of failure may not have been identified but has only been narrowed down to a subset of all potential risks. A general Bayesian formulation is investigated for the log-location-scale distribution family that includes most commonly used parametric lifetime distributions. Reparameterization is introduced for estimating the lifetime under the use condition stress and other parameters directly. The posterior analysis is done by Markov chain Monte Carlo sampling. The methodology is illustrated through the Weibull distributions, and a numerical example.