Residual life estimation under time-varying conditions based on a Wiener process
Residual life (RL) estimation plays an important role in prognostics and health management. In operating conditions, components usually experience stresses continuously varying over time, which have an impact on the degradation processes. This paper investigates a Wiener process model to track and p...
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Published in | Journal of statistical computation and simulation Vol. 87; no. 2; pp. 211 - 226 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Abingdon
Taylor & Francis
22.01.2017
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | Residual life (RL) estimation plays an important role in prognostics and health management. In operating conditions, components usually experience stresses continuously varying over time, which have an impact on the degradation processes. This paper investigates a Wiener process model to track and predict the RL under time-varying conditions. The item-to-item variation is captured by the drift parameter and the degradation characteristic of the whole population is described by the diffusion parameter. The bootstrap method and Bayesian theorem are employed to estimate and update the distribution parameters of 'a' and 'b', which are the coefficients of the linear drifting process in the degradation model. Once new degradation information becomes available, the RL distributions considering the future operating condition are derived. The proposed method is tested on Lithium-ion battery devices under three levels of charging/discharging rates. The results are further validated by a simulation method. |
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ISSN: | 0094-9655 1563-5163 |
DOI: | 10.1080/00949655.2016.1202953 |