Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 69, Issue 6, Pages 2858-2867Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2019.2924509
Keywords
Remaining useful life (RUL) prediction; rolling element bearings; time-varying Kalman filter
Funding
- National Natural Science Foundation of China [51575007, 51675035]
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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.
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