Abstract: Aiming at problems of a single physical feature being difficult to fully reflect fault information, the common fault feature extraction methods in machine learning were studied. A mixed feature fault diagnosis method based on complete ensemble empirical mode decomposition (CEEMD), dispersion entropy (DE) and Hjorth parameters was proposed. Firstly, the bearing signal was decomposed by CEEMD to obtain intrinsic mode function (IMF) components. Secondly, the sensitive IMFs were chosen by using the correlation with the original signal to calculate its DE and Hjorth parameters, and formed DE feature vector and Hjorth parameter matrix. Then the singular value decomposition (SVD) was applied to transform Hjorth parameter matrix into a singular value vector, and a mixed feature vector was formed with the singular value and DE feature vector. Finally, the least square support vector machine (LSSVM) based on particle swarm optimization (PSO) was used to train and identify different fault feature vectors. The results indicate that the method can accurately diagnose the fault type and degree of rolling bearings, and highlight the characteristics of different faults. Comparing with the method using single feature, the recognition rate reaches 100% after using this method, it can identify the fault information more accurately.

Key words:  rolling bearing;fault diagnosis; mixed characteristic extraction;complete ensemble empirical mode decomposition(CEEMD); dispersion entropy(DE); Hjorth parameter; singular value decomposition(SVD)

XIA Li-jian, LIU Xiao-ping, WANG Xin, et al. Rolling bearing fault diagnosis based on mixed characteristic of CEEMD dispersion entropy and Hjorth parameters［J］.Journal of Mechanical & Electrical Engineering, 2021,38(12):1564-1571.