Remaining useful life prediction approach for rolling element bearings based on optimized SVR model with reliable time intervals

The normal operation of rotating machineries depends on the health conditions of rolling element bearings. Once bearings fail, it will cause economic loss and even threaten operational safety. Therefore, it is essential to evaluate the health status of the bearings, where predicting the remaining useful life (RUL) is a quantitative evaluation method. To enable to learn from small-scale datasets of degraded bearings for RUL prediction, this work proposes to construct a support vector regression (SVR) prediction model based on Bayesian optimization (BO). An improved approach based on the $3\sigma$ interval was put forward to determine an optimal time to start prediction. The proposed method is verified on the bearing datasets in the IEEE PHM 2012 challenge. Experiment results verified that the time to start prediction is essential to building an accurate degradation model. Moreover, the BO algorithm is demonstrated to be superior in the optimization of SVR hyper-parameters, especially for low-dimensional data.