期刊
IEEE TRANSACTIONS ON RELIABILITY
卷 69, 期 1, 页码 401-412出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2018.2882682
关键词
Degradation; Predictive models; Data models; Kernel; Rolling bearings; Support vector machines; Adaptation models; Bearing degradation; prognostics; relevance vector machine; remaining useful life estimation; vibration monitoring
类别
资金
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1709208]
- National Natural Science Foundation of China [61673311, 51421004]
- National Program for Support of Top-Notch Young Professionals
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Frechet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.
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