Estimation and Optimization for Step-Stress Accelerated Degradation Tests Under an Inverse Gaussian Process with Tampered Degradation Model

This paper addresses the estimation and optimization problems for a step-stress accelerated degradation test (SSADT) when a product’s degradation follows an inverse Gaussian (IG) process. Developing a link model to connect the deterioration measurements at different stress levels is a primary concern for this design issue. In this paper, we propose a tampered degradation model and construct a new framework to model the effect of changing stress from a level to another on the degradation path. Parameter estimates are obtained by both maximum likelihood and Bayesian methods. Under the constraint that the total experimental cost does not exceed a pre-specified budget, the optimal settings such as sample size, measurement frequency, and the number of measurements at each stress level are obtained. Finally, a real-world example is analyzed to illustrate the application of the proposed method. A notable finding was that enlarging the difference between the two stress levels makes the test more efficient.