Optimal Design for Step-Stress Accelerated Degradation Test with Multiple Performance Characteristics Based on Gamma Processes

• Published in: Communications in statistics. Simulation and computation Vol. 43; no. 2; pp. 298 - 314
• Main Authors: Pan, Zhengqiang, Sun, Quan
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis Group 01.01.2014; Taylor & Francis Ltd
• Subjects: Accelerated tests; Asymptotic properties; Asymptotic variance; Computer simulation; Cost engineering; Degradation; Design optimization; Gamma process; Multiple performance characteristics; Optimal design; Optimization; Product design; Samples; Statistical analysis; Step-stress accelerated degradation test
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610918.2012.700749

Step-stress accelerated degradation test (SSADT) plays an important role in assessing the lifetime distribution of highly reliable products under normal operating conditions when there are not enough test units available for testing purposes. Recently, the optimal SSADT plans are presented based on an underlying assumption that there is only one performance characteristic. However, many highly reliable products usually have complex structure, with their reliability being evaluated by two or more performance characteristics. At the same time, the degradation of these performance characteristics would be always positive and strictly increasing. In such a case, the gamma process is usually considered as a degradation process due to its independent and nonnegative increments properties. Therefore, it is of great interest to design an efficient SSADT plan for the products with multiple performance characteristics based on gamma processes. In this work, we first introduce reliability model of the degradation products with two performance characteristics based on gamma processes, and then present the corresponding SSADT model. Next, under the constraint of total experimental cost, the optimal settings such as sample size, measurement times, and measurement frequency are obtained by minimizing the asymptotic variance of the estimated 100 qth percentile of the product's lifetime distribution. Finally, a numerical example is given to illustrate the proposed procedure.