A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

• Published in: IEEE transactions on reliability Vol. 69; no. 1; pp. 401 - 412
• Main Authors: Wang, Biao, Lei, Yaguo, Li, Naipeng, Li, Ningbo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerated tests; Adaptation models; Bearing degradation; Bearings; Data models; Degradation; Kernel; Machine learning; Predictive models; prognostics; relevance vector machine; remaining useful life estimation; Roller bearings; Rolling bearings; Support vector machines; Useful life; vibration monitoring
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2018.2882682

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 Fréchet 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.