Optimal burn-in policy for highly reliable products using inverse Gaussian degradation process

Abstract

Burn-in test is a manufacturing process implemented to identify and eliminate units with infant mortality before they are shipped to the customers. The traditional burn-in test over a short period of time to collect event data is rather inefficient. This decision problem can be solved if there is a suitable quality characteristic (QC) whose degradation over time can be related to the lifetime of the product. Optimal burn-in policies have been discussed in the literature assuming that the underlying degradation path follows a Wiener process or gamma process. However, the degradation paths of many products may be more appropriately modeled by an inverse Gaussian process that exhibits a monotone increasing pattern. Here, motivated by the numerous merits of the inverse Gaussian process, we first propose a mixed inverse Gaussian process to describe the degradation path of the product. Next, we present a decision rule for classifying a unit as typical or weak. A cost model is used to determine the optimal burn-in duration and the "scrap" cutoff level, and a simulation study is carried out to illustrate the proposed procedure.