Research on Degradation Representation of Rolling Bearings Based on a Constitution Health Index

Abstract

Roller bearings are commonly used components in rotating machinery and are pruned to be failure, which may cause huge economic losses and even casualties. Vibration signals of rolling bearings are signs of rotating machinery. Therefore, they can be used to describe the process of performance degradation. Unfortunately, the signals exhibit non-stationary and non-linear as well as highly non-monotonic behaviors as the bearing condition degrades especially under complex working conditions. In this paper, a method to construct a constitution health index based on vibration signals is presented. Firstly, 40 features in time domain, frequency domain, time-frequency domain and entropy domain are extracted individually. Then, the combined evaluation index is constructed based on the above 40 features in terms of monotonicity, robustness, predictability and correlation. Finally, by comparing the degradation fusion indexes of rolling bearings under different operating conditions with the traditional characteristics, the remaining useful life prediction method based on grey correlation analysis is proposed. The results suggest that the proposed method can effectively predict the RUL of rolling bearings with an acceptable degree of accuracy up to 90%, which outperforms the traditional features.