基于局部均值分解多尺度模糊熵和灰色相似关联度的滚动轴承故障诊断

doi:10.3969/j.issn.1000-1158.2018.02.19

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Abstract A rolling bearing fault diagnosis method based on local mean decomposition(LMD) of multi-scale fuzzy entropy and grey similar incidence is discussed. In this method, the fault signal is decomposed into several product functions (PF) adaptively, and the multi-scale fuzzy entropies of the PF components covering contain main fault information, which is calculated to get the fault feature vectors. By calculating the grey similar incidence of the sample to be identified and the standard fault pattern, it is realized that the judgement of rolling bearing fault types and damage degree. Compared with the method based on LMD fuzzy entropy and grey similar incidence, the experimental results show that the method based LMD multi-scale fuzzy entropy and grey similar incidence can identify rolling bearing running state efficiently and realize rolling bearing fault diagnosis.

Key words： metrology rolling bearing fault diagnosis local mean decomposition multi-scale fuzzy entropy grey similar incidence

Received: 27 June 2016 Published: 11 February 2018

PACS:

TB936

Corresponding Authors: Zong MENG E-mail: mzysu@ysu.edu.cn

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	MENG Zong
	ZHAO Dong-fang
	LI Jing
	XIONG Jing-ming
	LIU Shuang

Cite this article:

MENG Zong,ZHAO Dong-fang,LI Jing, et al. Rolling Bearing Fault Diagnosis Based on Local Mean Decomposition Multi-scale Fuzzy Entropy and Grey Similar Incidence[J]. Acta Metrologica Sinica, 2018, 39(2): 231-236.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2018.02.19 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2018/V39/I2/231

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