A Novel Fault Diagnosis Method for Rolling Bearing Based on EEMD-PE and Multiclass Relevance Vector Machine

The vibration signals of rolling bearing have the characteristics of nonlinearity and nonstationarity, in which a large number of working condition informations on rolling bearing are contained. By using vibration signals of rolling bearing, a novel rolling bearing fault diagnosis method based on ensemble empirical mode decomposition and permutation entropy (EEMD-PE) coupled with multiclass relevance vector machines (M-RVM) is proposed in this paper. The proposed method can decompose the vibration signals of rolling bearing to a series of intrinsic mode functions (IMFs) that contain different fault features of rolling bearing by using the self-adaptive decomposition ability of EEMD. Permutation entropy (PE) is used to extract the feature of each IMF and the fault feature vector of vibration singal is constituted by the PE values of IMFs. The M-RVM classifier is built by the feature vectors extracted from rolling bearing vibration samples in different fault modes. Moreover, the multiple faults identification result can be implemented by the MRVM classifier in the form of probability output. The experimental results demonstrate that the proposed fault diagnosis method is capable of effectively extracting fault features in vibration signal and improving the fault identification accuracy compared with the existing bearing fault diagnosis methods.