Rolling Bearing Fault Diagnosis based on Wavelet and Deep Wavelet Auto-encoder

Abstract

Aiming at the problem that it is difficult to accurately identify the fault severities and compound faults of rolling bearings，a method based on lifting dual-tree complex wavelet packet（LDTCWP）and deep wavelet auto-encoder （DWAE） is proposed. Firstly，the transfer learning strategy is introduced to extend the target data amount. Secondly，the vibration data of bearings is decomposed into three layers via lifting dual-tree complex wavelet packet. The sample entropy，permutation entropy and energy moment of each sub-band are calculated as raw eigenvectors. Finally，the raw eigenvectors are sent into DWAE for quadratic feature extraction and fault diagnosis. The fault diagnosis experiment results show that the method can effectively identify multiple fault types and multiple fault severities of bearings. Compared with traditional machine learning methods，the proposed method has better generalization ability，feature extraction ability and recognition ability in the case of insufficient target vibration data.

Keywords

• Rolling bearing;Lifting dual-tree complex wavelet packet;Deep wavelet auto-encoder;Transfer learning;Fault diagnosis