A universal LPC health monitoring method for rolling element bearings with ADSCI feature

Abstract

In this paper, a universal probability-based method is proposed for health stage division based on the feature exacted from different rolling element bearings. A new monitoring feature called Amplitude Difference of Strong and Carpet Impulse (ADSCI) is adopted to obtain the degradation information of bearings. Specifically, the high frequency information is utilized to get the degradation status, and a new impulse extractor method is developed to quantify the degree of the degradation. The Exponential Weibull distribution (EWD) is introduced to describe the probability distribution of the ADSCI feature in healthy stage and degradation stage, respectively. Then, a method named Likelihood Probability Comparison (LPC) is put forward to online detect the degradation point which is located between the healthy and degradation stage. For the LPC method, the EWD is utilized to calculate two likelihood probabilities regarding the healthy and degradation stage, respectively. The comparison result of the two likelihood probabilities is used to detect the degradation point. The performance of the proposed method is evaluated on the PRONOSTIA platform, and the comparison experiment with other features is performed. The result shows that the proposed ADSCI feature behaves stable for different bearings and the proposed LPC method is universal and effective for health monitoring of different bearings.