Product function correntropy and its application in rolling bearing fault identification

Rolling bearing as one of the key mechanical components plays a significant role in the field of industrial. To improve the ability of rolling bearing fault diagnosis under multi-rotating situation, this paper proposes a novel rolling bearing fault characteristic product function correntropy (PFC), and employs Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification of rolling bearings under multi-stationary working situations. Firstly, rolling bearing vibration acceleration signal is decomposed by Local Mean Decomposition (LMD) to extract product functions (PF). Secondly, PFC needs to be obtained. PFC is the solution of the correntropy mathematical model of primary signal and PF component that is modified by Correlation Coefficient Entropy (CCE) as amplitude modulation operator. Finally, drawing support from LSSVM, the fault identification is achieved. Through the bearing identification experiments in different rotating situations, it is verified that PFC generates higher diagnosis accuracy than traditional fault features. Meanwhile, it is proved that PFC has more robustness than traditional fault features under cross-mixed roller bearing rolling status. Above all, the higher efficiency and availability of LMD-PFC-LSSVM are confirmed from the experiment consequence. It can be concluded that LMD-PFC-LSSVM is a reliable technology for rolling bearing fault diagnosis online under complicated rolling conditions and possesses the broad application prospect.