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Open AccessArticle

A General Accelerated Degradation Model Based on the Wiener Process

by Le Liu 1,2, Xiaoyang Li 1,2, Fuqiang Sun 1,2,* and Ning Wang 1,2
1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Academic Editor: Mark T. Whittaker
Materials 2016, 9(12), 981; https://doi.org/10.3390/ma9120981
Received: 26 September 2016 / Revised: 27 November 2016 / Accepted: 30 November 2016 / Published: 6 December 2016
Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses. View Full-Text
Keywords: accelerated degradation testing; Wiener process; reliability; uncertainty; unit-to-unit variation accelerated degradation testing; Wiener process; reliability; uncertainty; unit-to-unit variation
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Liu, L.; Li, X.; Sun, F.; Wang, N. A General Accelerated Degradation Model Based on the Wiener Process. Materials 2016, 9, 981.

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