Feature Extraction Based on Hierarchical Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis

Bearing is the key part of mechanical equipment, which can support the rotating machinery running. It is crucial to diagnose bearing faults in time to ensure mechanical equipment working well. Effective feature extraction is an essential step in bearing fault diagnosis. However, the bearing vibration signals collected from the equipment usually contain interference, such as heavy noise. It is difficult to extract effective features from bearing vibration signals due to interference. To overcome this issue, a new feature extraction method for rolling bearing faults diagnosis is proposed based on hierarchical improved envelope spectrum entropy (HIESE). First, hierarchical decomposition is used to divide the bearing vibration signal into several hierarchical components. Second, the original feature set is obtained by calculating the improved envelope spectrum entropy (IESE) of each hierarchical component. Then, joint approximate diagonalization of eigenmatrices (JADE) is introduced to fuse the original features into a new one. Finally, support vector machines (SVMs) is taken to identify the bearing status. Two cases are used to test the proposed method. In case 1, the bearing vibration signals composed of different fault degrees and different fault types under two speeds are used to verify the proposed method. The recognition rate achieves 100%. In case 2, the bearing vibration signals, which consist of different fault types under three operation conditions, are analyzed. The recognition rate also achieves 100% in this case. Experimental results illustrated that the proposed method has good performance in feature extraction, which can provide a new method for feature extraction of rolling bearing.