A bearing fault diagnosis method based on multiscale dispersion entropy and GG clustering

Abstract

Fault diagnosis of rolling bearings depends on the construction of an effective index and a reasonable identification method of fault features. In this paper, an effective method to identify fault type and degree is presented. Firstly, each element in the training samples is decomposed by ensemble empirical mode decomposition, and then the dispersion entropy of the intrinsic mode function is calculated to construct the feature matrix of the training samples. Secondly, principal component analysis visually reduces the dimension of the feature matrix, and the Gath-Geva clustering method divides the reduced two-dimensional matrix to obtain the clustering center and category of the training sample features. Finally, the normalized clustering distance between the feature matrix of test samples and the clustering center of training samples is used to judge the membership. The effectiveness of the proposed method was verified by the Case Western Reserve University (CWRU) data set, the QPZZ-II platform, and Cincinnati Intelligent Maintenance Systems.