Research on a fault diagnosis method for rolling bearing based on improved multiscale range entropy and hierarchical prototype

A novel fault diagnosis method based on improved multiscale range entropy and hierarchical prototype (HP) is proposed in this paper. Firstly, considering that range entropy cannot analyze the complexity of time series from multiple perspectives, the coarse-grained process is combined with range entropy. In addition, to make the coarse-grained process more comprehensive, the selection of its starting point is improved. Secondly, to extract more feature information, the dimension reduction of eigenvectors is carried out by using singular value decomposition. Finally, HP is trained with the eigenvectors and its performance is tested. To test the performance of the proposed fault diagnosis method, testing bearing vibration signals collected by sensors from Case Western Reserve University and Southeast University are used for experimental analysis in this paper, and the experimental results show high accuracy of the proposed fault diagnosis method. To verify the suitability of the improvement proposal, the superiority in feature extraction ability and the classification capability of the classifier, the proposed fault diagnosis method is compared with another seven fault diagnosis methods. The results show that the proposed fault diagnosis method has the highest fault diagnosis accuracy.

Automated summary

A novel fault diagnosis method based on improved multiscale range entropy and hierarchical prototype (HP) is proposed, achieving high accuracy in diagnosing bearing vibration signals. Experimental results demonstrate the superiority of the method in feature extraction and classification capability, with the highest fault diagnosis accuracy obtained.