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Multivariate empirical mode decomposition method based on unbalanced channel information

Published:2024-03-06 author:LU Chunyuan, JIAO Hongyu, BU Wanghui. Browse: 451 Check PDF documents

Multivariate empirical mode decomposition method based on

unbalanced channel information

LU Chunyuan1, JIAO Hongyu2, BU Wanghui3

(1.School of Mechanical and Electrical Engineering, Suzhou Vocational University, Suzhou 215104, China; 2.School of Automotive

Engineering, Changshu Institute of Technology, Suzhou 215500, China; 3.School of Mechanical Engineering,

Tongji University, Shanghai 200092, China)

Abstract: In order to address the problem of low diagnostic accuracy caused by imbalanced fault information in multichannel vibration signals of bearings, a feature extraction and diagnosis method considering multichannel information imbalanced multivariate empirical mode decomposition (MEMD)was proposed. First, the shortcomings of traditional multivariate empirical mode decomposition in randomly selecting the mapping direction were analyzed, and a strategy was designed to adaptively adjust the mapping direction based on the imbalance of fault information between channels, so that the component signal contained more fault information, and a feature space based on the results of multivariate mode decomposition was constructed. Then, based on redundant attribute projection method, the fault features extracted from multiple channels were fused to obtain the essential fault features of multichannel fusion. Finally, the back propagation (BP) neural network was used for fault pattern recognition, a three-layer neural network structure was designed, and error back propagation method was used for parameter training. A bearing fault diagnosis process based on BP neural network was developed. The research result shows that the improved MEMD has a clearer class boundary for feature extraction compared to traditional method, indicating that the improved method can extract more representative fault features. From the perspective of diagnostic accuracy, comparing with the traditional multimodal decomposition and complete integrated symplectic geometry decomposition, the improved method possesses the highest diagnostic accuracy of 99.5%. It is verified by the experimental result that the improved method is feasible in multichannel fault diagnosis, and possesses a certain progressiveness in terms of diagnosis accuracy.
Key words: feature extraction and diagnosis of bearing faults; multichannel sampling; information imbalance; multivariate empirical mode decomposition(MEMD); redundant attribute projection; back propagation(BP) neural network; feature space construction; essential fault characteristics

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