Fault diagnosis of rolling bearings based on IRCMNDE and NNCHC

In view of the problem that information loss and false information may occur dur‑ ing the coarse‑graining process of multi‑scale dispersion entropy（MDE），which make it difficult to extract bearing fault information comprehensively， a rolling bearing fault diagnosis method based on improved refined composite multi ‑ scale normalized dispersion entropy（IRCMNDE）and nearest neighbor convex hull classification(NNCHC)was proposed. The refined composite multi? scale dispersion entropy(RCMDE)was introduced,and the average value in the coarse ? graining process was replaced by the maximum value to represent the data segments information, which can overcome the shortcomings of the traditional coarse ?graining process and highlight the fault characteristics. Through the normalization operation to reduce the influence of the selection of different parameters on the entropy value, IRCMNDE was acquired as feature samples; NNCHC was used to classify the feature samples to realize bearing fault diagnosis. Experimental results showed that the proposed method can achieve 98. 98% fault identification accuracy. Com? pared with the methods based on MDE(fault identification accuracy was 95. 99%)and RCMDE (fault identification accuracy was 97. 60%),the proposed method can extract the fault feature in? formation of rolling bearings more accurately and improve the accuracy of fault classification. © 2022 BUAA Press. All rights reserved.