基于最大幅值变分模态分解和均方根熵的滚动轴承故障诊断

doi:10.3969/j.issn.1000-1158.2020.04.011

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Abstract The setting of modal number in the variational mode decomposition is very important for the decomposition results, based on this, a method to obtain the optimal decomposition layer number is proposed. The method is based on amplitude characteristics of the instantaneous frequencies and determines the optimal decomposition parameter by analyzing the relationship between the maximum amplitude of each component in the variational mode decomposition process. The root mean square entropy can reflect the energy of different vibration signals and is used as the characteristic parameter of the fault. And a fault classification model is established by optimized support vector machine to realize fault patterns classification. The fault diagnosis method based on maximum amplitude variational mode decomposition and root mean square entropy is applied to the measured signal of rolling bearings. The results show that the method based on maximum amplitude variational mode decomposition and root mean square entropy can identify rolling bearing running state efficiently and realize rolling bearing fault diagnosis. The recognition accuracy of this method is 98.75%.

Key words： metrology rolling bearing fault diagnosis variational mode decomposition root mean square entropy support vector machine

Received: 11 July 2018

PACS:	TB936
	TB973

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	Articles by authors
	MENG Zong
	YUE Jian-hui
	XING Ting-ting
	LI Jing
	YIN Na

Cite this article:

MENG Zong,YUE Jian-hui,XING Ting-ting, et al. Rolling Bearing Fault Diagnosis Based on Maximum Amplitude Variational Mode Decomposition and Root Mean Square Entropy[J]. Acta Metrologica Sinica, 2020, 41(4): 455-460.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.04.011 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I4/455

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