Bearing Fault Diagnosis Based on Refined Composite Multi‐Scale Dispersion Entropy and Extenics

Abstract

Considering the difficulty of extracting bearing fault signals of pumping unit and the complexity of diagnosis, a bearing fault diagnosis method based on refined composite multi‐scale dispersion entropy (RCMDE) and Extenics is proposed. Firstly, RCMDE is used to extract the features of bearing data. Using Fisher Ratio to reduce the dimensionality of RCMDE features and select them. The selected RCMDE features are used as the feature parameters of the matter element models to construct the classical domains and node domain of the four matter element models, and build the matter element models of the four healthy states of the bearing. Finally, the sample sets to be tested are input into the four health state models to calculate the comprehensive correlation degree value. The fault diagnosis is realized according to the principle of maximum comprehensive correlation degree. The results show that the detection rate of the proposed method for the four health states of the bearing is 98.8%. In order to verify the superiority of the proposed method, the method proposed in this paper is compared with the fault diagnosis method based on the combination of MDE and extenics, and the results verify the superiority of the proposed method in this paper. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.