Bayesian optimal design of step stress accelerated degradation testing

作     者：Xiaoyang Li Mohammad Rezvanizaniani Zhengzheng Ge Mohamed Abuali Jay Lee 

作者机构：Science and Technology on Reliability and Environmental Engineering Laboratory Beihang University NSF Industry/University Cooperative Research Center on Intelligent Maintenance SystemsUniversity of Cincinnati Beijing Institute of Electronic System Engineering 

基　　金：supported by the National Natural Science Foundation of China(61104182) 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2015年第26卷第3期

页      码：502-513页

摘      要：This study presents a Bayesian methodology for de- signing step stress accelerated degradation testing （SSADT） and its application to batteries. First, the simulation-based Bayesian de- sign framework for SSADT is presented. Then, by considering his- torical data, specific optimal objectives oriented Kullback-Leibler （KL） divergence is established. A numerical example is discussed to illustrate the design approach. It is assumed that the degrada- tion model （or process） follows a drift Brownian motion; the accele- ration model follows Arrhenius equation; and the corresponding parameters follow normal and Gamma prior distributions. Using the Markov Chain Monte Carlo （MCMC） method and WinBUGS software, the comparison shows that KL divergence is better than quadratic loss for optimal criteria. Further, the effect of simulation outiiers on the optimization plan is analyzed and the preferred sur- face fitting algorithm is chosen. At the end of the paper, a NASA lithium-ion battery dataset is used as historical information and the KL divergence oriented Bayesian design is compared with maxi- mum likelihood theory oriented locally optimal design. The results show that the proposed method can provide a much better testing plan for this engineering application.

主 题 词：accelerated testing Bayesian theory KL divergence,degradation optimal design battery. 

学科分类：0808[工学-自动化类] 08[工学] 

核心收录：

D　O　I：10.1109/JSEE.2015.00058

馆 藏 号：203459156...