基于LCD-LLTSA的电动汽车电机轴承故障特征频率提取

doi:10.3969/j.issn.1000-1158.2020.10.14

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Abstract In order to extracting electric car motor bearing fault feature frequency effectively, a fault feature frequency extracting method of electric car motor bearing based on local characteristic-scale decomposition (LCD) , linear local tangent space algorithm (LLTSA) and envelope spectrum analysis is introduced. Firstly, electric car bearing original fault signals are decomposed into several intrinsic scale component (ISC) with different frequency band components through LCD. And then, the LLTSA was used to reduce the dimension of the matrix construct by ISC components, and then a new fault signal can obtain by a low dimension matrix which obtained by LLTSA. Finally, the fault frequency can be identified accurately by the envelope spectrum. The experimental results of simulation signal and experiment signal show that the proposed method can identify different state effectively and has a certain superiority.

Key words： metrology rolling bearings fault diagnosis feature frequency local characteristic-scale decomposition linear local tangent space algorithm

Received: 21 November 2018 Published: 10 October 2020

PACS:	TB936
	TB973

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	Articles by authors
	SHI Su-min
	YANG Chun-chang
	WANG Fei

Cite this article:

SHI Su-min,YANG Chun-chang,WANG Fei. Electric Car Motor Bearing Fault Feature Frequency Extracting Method Based on LCD and LLTSA[J]. Acta Metrologica Sinica, 2020, 41(10): 1267-1272.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.10.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I10/1267

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