A Comparative Study on the Data-driven Based Prognostic Approaches for RUL of Rolling Bearings

With the condition monitoring equipment becoming more sophisticated, data-driven based prognostic approaches for remaining useful life (RUL) are emerging. This paper introduces three classical prognostic approaches and verifies the effectiveness through the whole-life cycle experimental data of degenerated rolling bearings. The result shows that the prediction of the methods based on probability statistics will be greatly affected, if the prior parameters are inaccurate. And the degradation model cannot be adapted to individual bearing accurately. The prognostic method based on artificial intelligence and condition monitoring is more accurate in the case of a small number of training samples, and it will output a remaining useful life prediction interval with higher reliability. Therefore, by combining with other models, improving the intelligent algorithm to enhance the accuracy of its RUL prediction is the key to solve the problem about online prognostic.