一种改进的自适应多元变分模态分解轴承故障信号特征提取方法

doi:10.3969/j.issn.1000-1158.2022.10.13

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Abstract Aiming at the nonlinearity and non-stationarity of bearing signals in practical engineering, an adaptive multi-variable mode decomposition algorithm is proposed. The decomposition effect of multivariate variational modes is mainly related to the number of intrinsic modes k and penalty parameter α. In order to solve the influence of artificial empirical parameter setting on the decomposition results of multivariate signals, an adaptive signal decomposition algorithm is proposed. The specific contents are as follows: Firstly, the hybrid gray wolf algorithm is combined with the multivariate variational mode decomposition algorithm, and the minimum mode overlap component index is proposed, which is used as the fitness function to seek the optimal solution of(k, α). According to the optimal solution, the multivariate signals are decomposed and the fault features are extracted. Simulation signals and actual data are used to verify the effectiveness and accuracy of the proposed method. By comparing with multivariate empirical mode decomposition (MEMD) and cascade variational mode decomposition, the effectiveness and practicability of the proposed method in rolling bearing fault feature extraction are verified.

Key words： metrology rolling bearing fault diagnosis multivariate variational mode decomposition hybrid grey wolf optimization algorithm mode overlap component

Received: 12 October 2021 Published: 14 October 2022

PACS:	TB936
	TB973

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	Articles by authors
	SHI Pei-ming
	ZHANG Hui-chao
	YI Si-ying
	HAN Dong-ying

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SHI Pei-ming,ZHANG Hui-chao,YI Si-ying, et al. An Improved Feature Extraction Method of Bearing Fault Signal Based on Adaptive Multivariate Variational Mode Decomposition[J]. Acta Metrologica Sinica, 2022, 43(10): 1326-1334.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.10.13 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I10/1326

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