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

TLDR

Remaining useful life prediction of rolling element bearings is crucial for reducing unplanned maintenance and enhancing machine reliability, availability, and safety. This study proposes a hybrid prognostics approach to predict the remaining useful life of rolling element bearings. The method first sparsely represents degradation data with relevance vector machine regressions using various kernel parameters, then applies exponential degradation models coupled with the Fréchet distance to adaptively estimate RUL, evaluated on vibration data from accelerated tests and the PRONOSTIA dataset. Experimental results show that the hybrid approach improves the accuracy and convergence of RUL predictions for rolling element bearings.

Abstract

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.