Bayesian calculation of cost optimal burn-in test durations for mixed exponential populations

The burn-in process is a part of the production process whereby manufactured products are operated for a short period of time before release. In this paper, a Bayesian method is developed for calculating the optimal burn-in duration for a batch of products whose life distribution is modeled as a mixture of two (denoted ‘strong’ and ‘weak’) exponential sub-populations. The criteria used is the minimization of a total expected cost function reflecting costs related to the burn-in process and to product failures throughout a warranty period. The expectation is taken with respect to the mixed exponential failure model and its parameters. The prior distribution for the parameters is constructed using a beta density for the mixture parameter and independent gamma densities for the failure rate parameters of the sub-populations. It is assumed that the optimal burn-in time is selected in advance and remains fixed throughout the burn-in process. When additional failure information is available prior to the burn-in process, the minimization of posterior total cost is used as the criteria for selecting the optimal burn-in time. Expressions for the joint posterior distribution and cost are provided for the case of both complete and truncated data. The method is illustrated with an example.