Optimal design of hybrid accelerated test based on the Inverse Gaussian process model

Accelerated life testing (ALT) and accelerated degradation testing (ADT) are two widely used accelerated testing (AT) methods for reliability evaluation of highly reliable products. However, in many cases, it is difficult to meet the estimation requirements only using ALT or ADT because of the nature of data and the test constraints. Since both ALT and ADT data provide useful reliability information, it would be quite valuable to use both of them for reliability estimation. In this paper, an optimal testing plan is proposed for the first time for such a hybrid AT under several experimental design constraints. An Inverse Gaussian (IG) process and the corresponding lifetime distribution are used to model the degradation process and the lifetime of the product, respectively. The strategy of allocating the test units to ALT and ADT and determining the data collection strategy in the experiment are presented. The objective is to maximize the estimation precision of the p-quantile of the product's lifetime under the normal use condition. A study on the stress relaxation of a type of electrical connector is conducted to illustrate the value of the proposed method in practice.

Automated summary

This paper proposes an optimal testing plan for a hybrid accelerated testing, utilizing an Inverse Gaussian process and lifetime distribution model to maximize estimation precision of the product's lifetime under normal use condition. An experimental design with test unit allocation and data collection strategy is presented for reliability evaluation.