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Abstract：
Rolling bearings are the key components of rotating machinery. Thus, the prediction of remaining useful life (RUL) is vital in condition-based maintenance (CBM). This paper proposes a new method for RUL prediction of bearings based on time-varying Kalman filter, which can automatically match different degradation stages of bearings and effectively realize the prediction of RUL. The evolution of monitoring data in normal and slow degradation stages is a linear trend, and the evolution in accelerated degradation stage is nonlinear. Therefore, Kalman filter models based on linear and quadratic functions are established. Meanwhile, a sliding window relative error is constructed to adaptively judge the bearing degradation stages. It can automatically switch filter models to process monitoring data at different stages. Then, the RUL can be predicted effectively. Two groups of bearing run-to-failure data sets are utilized to demonstrate the feasibility and validity of the proposed method.
Keyword：
Reprint Author's Address：
Source ：
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN： 0018-9456
Year： 2020
Issue： 6
Volume： 69
Page： 2858-2867
5 . 6 0 0
JCR@2022
ESI Discipline： ENGINEERING;
ESI HC Threshold：115
Cited Count：
WoS CC Cited Count： 172
SCOPUS Cited Count： 188
ESI Highly Cited Papers on the List： 34 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 5
Affiliated Colleges：
