期刊
IEEE TRANSACTIONS ON RELIABILITY
卷 68, 期 2, 页码 689-709出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2018.2829844
关键词
Degradation; life prognostics; multi-phase Wiener process; reliability; remaining useful life (RUL) estimation
类别
资金
- National Natural Science Foundation of China (NSFC) [61573365, 61025014, 61490701, 61751307, 61473094, 61703244]
- Research Fund for the Taishan Scholar Project of Shandong Province of China [LZB2015-162]
- National Key Research and Development Program of China [2017YFA0700300]
- NSFC [61773386, 61733009, 61522309, 61473163]
- Special Fund of Suzhou-Tsinghua Innovation Leading Action [2016SZ0202]
- Young Elite Scientists Sponsorship Program (YESS) of China Association for Science and Technology (CAST) [2016QNRC001]
Due to the inner deteriorating mechanism or the mutant environmental stress, the degradation systems with multiphase features have frequently been encountered in engineering practice. The key issue for prognostics of such systems is to account for the impact of the changing-point variability and the associated degradation state at this point on the progression of the degradation process. However, current studies usually treat the degradation state at the change point as a fixed value rather a random variable. Thus, it is still challenging to predict the lifetime of such multiphase degrading systems. To this end, we first formulate a general degradation modeling framework based on a two-phase Wiener process. In prognostics, we take into full account the uncertainty of the degradation state at the changing point and then derive the analytical expressions of the lifetime and remaining useful life under the concept of the first passage time. The derived results are distinguished from existing results limited to the fixed state at the changing point. Furthermore, we extend our approach and results to cases with unit-to-unit variability and multiple phases. To facilitate the model implementation, we propose both offline and online methods for parameter identification, which make full use of the historical data and the in-service data. Finally, a numerical simulation and a practical case study are provided for illustration.
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