Bayesian Methods for Accelerated Destructive Degradation Test Planning

Abstract

Accelerated Destructive Degradation Tests (ADDTs) provide timely product reliability information in practical applications. This paper describes Bayesian methods for ADDT planning under a class of nonlinear degradation models with one accelerating variable. We use a Bayesian criterion based on the estimation precision of a specified failure-time distribution quantile at use conditions to find optimum test plans. A large-sample approximation for the posterior distribution provides a useful simplification to the planning criterion. The general equivalence theorem (GET) is used to verify the global optimality of the numerically optimized test plans. Optimum plans usually provide insight for constructing compromise plans which tend to be more robust, and practically useful. We present a numerical example with a log-location-scale distribution to illustrate the Bayesian test planning methods, and to investigate the effects of the prior distribution and sample size on test planning results.