A dual-stress Bayesian Weibull accelerated life testing model

TL;DR

This paper introduces a Bayesian accelerated life testing model using Weibull distribution and a generalized Eyring transformation, capable of handling multiple stressors, with application demonstrated via MCMC-based posterior inference.

Contribution

It presents a novel dual-stress Bayesian Weibull model that extends existing models to incorporate multiple stressors, enhancing flexibility in life testing analysis.

Findings

Model successfully incorporates multiple stressors.

MCMC methods effectively estimate posterior distributions.

Application demonstrates practical utility of the model.

Abstract

In this paper, a Bayesian accelerated life testing model is presented. The Weibull distribution is used as the life distribution and the generalised Eyring model as the time transformation function. This is a model that allows for the use of more than one stressor, whereas other commonly used acceleration models, such as the Arrhenius and power law models, allow for only one stressor. The generalised Eyring-Weibull model is used in an application, where MCMC methods are utilised to generate samples for posterior inference.