基于多尺度高阶奇异谱熵的信号特征提取方法

doi:10.3969/j.issn.1000-1158.2019.05.18

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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.

Key words： metrology variational mode decomposition fault diagnosis multi-scale deep belief network

Received: 11 September 2017 Published: 01 September 2019

PACS:	TB936
	TB973

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.05.18 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I5/848

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