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Optimum step-stress accelerated degradation test for Wiener degradation process under constraints
Authors:Cheng-Hung Hu  Ming-Yung Lee  Jen Tang
Institution:1. Department of Industrial and Information Management, National Cheng Kung University, Tainan City, 701, Taiwan;2. Statistics and Informatics Department, Providence University, Taichung City, 43301, Taiwan;3. Krannert Graduate School of Management, Purdue University, 403 West State Street, West Lafayette, IN 47907-2056, United States
Abstract:To assess a product's reliability for subsequent managerial decisions such as designing an extended warranty policy and developing a maintenance schedule, Accelerated Degradation Test (ADT) has been used to obtain reliability information in a timely manner. In particular, Step-Stress ADT (SSADT) is one of the most commonly used stress loadings for shortening test duration and reducing the required sample size. Although it was demonstrated in many previous studies that the optimum SSADT plan is actually a simple SSADT plan using only two stress levels, most of these results were obtained numerically on a case-by-case basis. In this paper, we formally prove that, under the Wiener degradation model with a drift parameter being a linear function of the (transformed) stress level, a multi-level SSADT plan will degenerate to a simple SSADT plan under many commonly used optimization criteria and some practical constraints. We also show that, under our model assumptions, any SSADT plan with more than two distinct stress levels cannot be optimal. These results are useful for searching for an optimum SSADT plan, since one needs to focus only on simple SSADTs. A numerical example is presented to compare the efficiency of the proposed optimum simple SSADT plans and a SSADT plan proposed by a previous study. In addition, a simulation study is conducted for investigating the efficiency of the proposed SSADT plans when the sample size is small.
Keywords:Reliability  Accelerated Degradation Test  Step-Stress ADT  Wiener process  Inverse Gaussian distribution
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