Fault diagnosis method for rolling bearings based on minimum entropy deconvolution and autograms

被引：0

作者：

Wang X. ^{[1
]}

Zheng J. ^{[1
]}

Pan H. ^{[1
]}

Tong J. ^{[1
]}

Liu Q. ^{[1
]}

Ding K. ^{[2
]}

机构：

[1] School of Mechanical Engineering, Anhui University of Technology, Maanshan

[2] China Special Equipment Inspection and Research Institute, Beijing

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 18期

关键词：

Autogram; Demodulation band; Fast spectral kurtogram; Minimum entropy deconvolution(MED); Rolling bearing;

D O I：

10.13465/j.cnki.jvs.2020.18.015

中图分类号：

学科分类号：

摘要：

The vibration signal of rolling bearings generally has low signal-to-noise ratio and is often contaminated by strong non-Gaussian noise. How to accurately select the demodulation frequency band is always a difficult problem in the fault diagnosis of rolling bearings. The autogram is a new optimal band selection method. By calculating the kurtosis of unbiased autocorrelation of the squared envelope of a demodulated signal, the demodulation band and fault frequency can be effectively detected. But, the autogram is susceptible to noise and the included fault feature is not obvious. In view of that, a new fault diagnosis method for rolling bearings based on minimum entropy deconvolution (MED) and autograms was proposed. The method can effectively highlight the fault characteristics and obtain the best band for demodulation. The proposed method was compared with the fast spectral kurtosis approach and other existing methods by analysing the simulation and experimental data of rolling bearings. The results show that the proposed fault diagnosis method for rolling bearings can accurately detect the demodulation frequency band, highlight the fault frequency and improve the effect of fault detection. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：118 / 124and131

共 21 条

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