基于循环谱分析的鲁棒性滚动轴承故障特征提取方法

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Abstract Cyclic stationary analysis is one of the important methods for rolling bearing fault feature extraction. However, the bearing fault feature cannot be effectively extracted for the excessive irrelevant interference component. A robust rolling bearing fault feature extraction method based on cyclic spectrum analysis has been proposed to solve the problem in this paper. The random component of signal can be extracted by discrete random separation (DRS), and then the vibration energy sequence will be calculated by Teager energy operator (TEO) though the random component. With the fast spectral correlation analysis, the energy intensity of each cycle frequency (order) slice can be characterized by energy difference coefficient based on energy entropy. The influence of irrelevant interference component can be reduced by entropy weighting. Then, the fault feature of rolling bearing can be effectively extracted. It verified the advantage of this method in the application of rolling bearing fault diagnosis by experimental comparison of fast spectral kurtosis, fast spectral correlation and fast spectral correlation based on total variation de-noising.

Key words： rolling bearing cyclic spectrum analysis vibration energy fault feature extraction

Received: 20 October 2020 Published: 28 March 2022

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	Articles by authors
	YAN Yunhai
	GUO Yu
	WU Xing

Cite this article:

YAN Yunhai,GUO Yu,WU Xing. Robust rolling bearing fault feature extraction method based on cyclic spectrum analysis[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(6): 1-7.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I6/1

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