Feature extraction for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1.5 dimension spectrum

被引：0

作者：

Tang, Gui-Ji ^{[1
]}

Wang, Xiao-Long ^{[1
]}

机构：

[1] School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2015年 / 34卷 / 12期

关键词：

1.5 dimension spectrum; Deconvolution; Feature extraction; Incipient fault; Rolling bearing;

D O I：

10.13465/j.cnki.jvs.2015.12.014

中图分类号：

学科分类号：

摘要：

Early fault feature of rolling bearing is very weak and affected by environment noise seriously, so it is difficult to be drawn. Aiming at solving this problem, maximum correlated kurtosis deconvolution (MCKD) was introduced to the field of fault diagnosis for rolling bearing and combining with the 1.5 dimension spectrum, a feature extraction method for rolling bearing incipient fault was proposed. The fault signal was processed by MCKD method and the envelope of its deconvolution signal was calculated, then the envelope signal was analysed using 1.5 dimension spectrum method. The bearing fault was judged by analyzing the frequency components of 1.5 dimension envelope spectrum. The analysis results of simulated and measured fault signals of rolling bearings show that the method can effectively extract the feature frequency information of incipient fault and has a certain reliability. ©, 2015, Chinese Vibration Engineering Society. All right reserved.

引用

页码：79 / 84

页数：5

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