基于自相关分析与MCKD的滚动轴承早期故障诊断

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Abstract Rolling bearing early faults often present a non-stationary signal, weak regulation.The fault feature component is not outstanding and there is strong background noise, which makes effective extraction of bearing fault features very difficult.To solve this problem, a fault diagnosis method of rolling bearings based on autocorrelation analysis and MCKD was proposed in this paper.First of all, the use of the biased estimate autocorrelation analysis method of the bearing signal makes a preliminary analysis, realizes the inhibition effect of noise in the signal component, then MCKD solution of the convolution algorithm of the signals was used for further analysis, highlighting the original impact signal components and further de-noising.The signal-to-noise ratio was further improved.Finally, signal envelope spectrum analysis was used to determine the bearing fault location.The simulations and the analysis results of the measured data show that the proposed method can effectively extract the characteristic information of the fault signal and has certain validity.

Key words： maximum correlated kurtosis deconvolution(MCKD) autocorrelation analysis rolling bearing incipient fault diagnosis feature extraction

Received: 29 June 2018 Published: 15 December 2019

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	ZHU Xiaoyan
	WANG Yongjie

Cite this article:

ZHU Xiaoyan,WANG Yongjie. A method of incipient fault diagnosis of bearings based on autocorrelation analysis and MCKD[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(24): 183-188.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I24/183

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