1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, China
2. Tangshan Power Supply Company, State Grid Jibei Power Co. Ltd., Tangshan, Hebei 063000, China
3. Research Institute of Economic and Technical, State Grid Jibei Power Co. Ltd., Beijing 102209, China
Abstract：A feature extraction method based on variational mode decomposition (VMD) for high-order singular spectral entropy is proposed and applied to fault diagnosis of rolling bearings. Firstly, the fourth-order cumulant slice is used to replace the covariance matrix of singular spectrum entropy analysis (SSEA), and the VMD decomposition method is introduced to multi-scale. The multi-resolution high-order singular spectrum entropy analysis of bearing vibration signal is proposed. Through signal analysis, VMD solves the problem of modal aliasing and can realize signal filtering. At the same time, the entropy feature vector extracted by the method enhances the robustness of phase space reconstruction parameters. By comparing with the wavelet singular spectrum extraction feature, the results show that the proposed method is more advantageous in overcoming the frequency aliasing phenomenon and the small overall dispersion of feature points. Finally, the classification of faults is realized by combining the deep belief network classifier. The validity and feasibility of the proposed method are verified by the results.
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2019, Vol. 40 
