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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摘要为有效提取出电动汽车电机轴承故障特征频率,将局部特征尺度分解、线性局部切空间排列和包络分析进行结合,用于电动汽车电机轴承的故障特征频率的提取。首先利用局部特征尺度分解对电动汽车电机轴承故障信号进行分解,得到若干个内禀尺度分量;然后利用线性局部切空间排列对由内禀尺度分量构成的矩阵进行降维处理,得到低维矩阵并以此进行信号重构;最后对重构信号进行包络谱分析,获得故障特征频率。仿真信号和实验信号的实验结果验证了方法的有效性。

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	史素敏
	杨春长
	王斐

关键词 ：计量学, 滚动轴承, 故障诊断, 特征频率, 局部特征尺度分解, 线性局部切空间排列

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

收稿日期: 2018-11-21 发布日期: 2020-10-10

PACS:	TB936
	TB973

基金资助:河北省自然科学基金(E2016506003)

作者简介: 史素敏(1987-), 女, 河南安阳人, 商丘学院讲师, 硕士, 主要从事机械设计制造与机电控制研究。Email:shisumin521@163.com

引用本文:

史素敏,杨春长,王斐. 基于LCD-LLTSA的电动汽车电机轴承故障特征频率提取[J]. 计量学报, 2020, 41(10): 1267-1272.
SHI Su-min,YANG Chun-chang,WANG Fei. Electric Car Motor Bearing Fault Feature Frequency Extracting Method Based on LCD and LLTSA. Acta Metrologica Sinica, 2020, 41(10): 1267-1272.

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

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