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Article

A Bayesian Optimal Design for Sequential Accelerated Degradation Testing

by 1,2, 1,2, 1,2,* and 1,2
1
Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
2
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(7), 325; https://doi.org/10.3390/e19070325
Received: 16 May 2017 / Revised: 21 June 2017 / Accepted: 27 June 2017 / Published: 1 July 2017
(This article belongs to the Special Issue Maximum Entropy and Bayesian Methods)
When optimizing an accelerated degradation testing (ADT) plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, when the difference between a prior distribution and actual situation is large, the existing Bayesian optimal design might cause some over-testing or under-testing issues. For example, the implemented ADT following the optimal ADT plan consumes too much testing resources or few accelerated degradation data are obtained during the ADT. To overcome these obstacles, a Bayesian sequential step-down-stress ADT design is proposed in this article. During the sequential ADT, the test under the highest stress level is firstly conducted based on the initial prior information to quickly generate degradation data. Then, the data collected under higher stress levels are employed to construct the prior distributions for the test design under lower stress levels by using the Bayesian inference. In the process of optimization, the inverse Gaussian (IG) process is assumed to describe the degradation paths, and the Bayesian D-optimality is selected as the optimal objective. A case study on an electrical connector’s ADT plan is provided to illustrate the application of the proposed Bayesian sequential ADT design method. Compared with the results from a typical static Bayesian ADT plan, the proposed design could guarantee more stable and precise estimations of different reliability measures. View Full-Text
Keywords: Bayesian optimal design; accelerated degradation testing (ADT); sequential decision; D-optimality; inverse Gaussian process Bayesian optimal design; accelerated degradation testing (ADT); sequential decision; D-optimality; inverse Gaussian process
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MDPI and ACS Style

Li, X.; Hu, Y.; Sun, F.; Kang, R. A Bayesian Optimal Design for Sequential Accelerated Degradation Testing. Entropy 2017, 19, 325. https://doi.org/10.3390/e19070325

AMA Style

Li X, Hu Y, Sun F, Kang R. A Bayesian Optimal Design for Sequential Accelerated Degradation Testing. Entropy. 2017; 19(7):325. https://doi.org/10.3390/e19070325

Chicago/Turabian Style

Li, Xiaoyang, Yuqing Hu, Fuqiang Sun, and Rui Kang. 2017. "A Bayesian Optimal Design for Sequential Accelerated Degradation Testing" Entropy 19, no. 7: 325. https://doi.org/10.3390/e19070325

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