基于MCKD和VMD的滚动轴承微弱故障特征提取

夏均忠,赵磊,白云川,于明奇,汪治安

振动与冲击 ›› 2017, Vol. 36 ›› Issue (20) : 78-83.

PDF(1342 KB)
PDF(1342 KB)
振动与冲击 ›› 2017, Vol. 36 ›› Issue (20) : 78-83.
论文

基于MCKD和VMD的滚动轴承微弱故障特征提取

  • 夏均忠,赵磊,白云川,于明奇,汪治安
作者信息 +

Feature extraction for rolling element bearing weak fault based on MCKD and VMD

  • XIA Jun-zhong,ZHAO Lei,BAI Yun-chuan,YU Ming-qi,WANG Zhi-an
Author information +
文章历史 +

摘要

针对滚动轴承早期故障特征非常微弱,易受随机噪声和其他信号干扰而难以提取等现象,提出了用最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution,MCKD)和变分模态分解(Variational Mode Decomposition,VMD)相结合的方法提取滚动轴承故障特征。首先用MCKD进行信号增强,然后利用VMD得到一系列模态,应用互相关系数和峭度准则筛选包含故障信息较为丰富的模态进行重构降噪,最后对重构信号进行包络解调提取故障特征。通过仿真分析和轴承故障模拟实验验证了该方法的有效性,可以精确地分离轴承故障振动信号的不同频率成分。

Abstract

The fault feature of rolling element bearing in early failure period is so weak and susceptible to random noise and other signal interference that it’s very difficult to be extracted,so combined maximum correlated kurtosis deconvolution with variational mode decomposition for extracting rolling element bearing fault feature. Firstly enhanced the signal by MCKD and decomposed into several modes by VMD,then reconstructed and reduced noise with the mode,which selected by the comparative correlation coefficient and kurtosis criterion,finally used the envelope demodulation to extract fault feature. The simulating signal analysis and bearing fault simulator show the validity of the method:this method can accurately separate different frequency components of bearing fault vibration signals.
 

关键词

滚动轴承 / 最大相关峭度解卷积 / 变分模态分解 / 互相关系数 / 峭度准则

Key words

rolling element bearing / MCKD / VMD / correlation coefficient / kurtosis criterion

引用本文

导出引用
夏均忠,赵磊,白云川,于明奇,汪治安. 基于MCKD和VMD的滚动轴承微弱故障特征提取[J]. 振动与冲击, 2017, 36(20): 78-83
XIA Jun-zhong,ZHAO Lei,BAI Yun-chuan,YU Ming-qi,WANG Zhi-an. Feature extraction for rolling element bearing weak fault based on MCKD and VMD[J]. Journal of Vibration and Shock, 2017, 36(20): 78-83

参考文献

[1] Mcdonald G L,Zhao Q,Zuo M J.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection [J].Mechanical Systems and Signal Processing,2012,33:237-255.
[2] 唐贵基,王晓龙.最大相关峭度解卷积结合1.5维谱的滚动轴承早期故障特征提取方法[J].振动与冲击,2015,34(12):79-84.
TANG Gui-ji,WANG Xiao-long.Feature extraction for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1.5 dimension spectrum[J].Journal of Vibration and Shock,2015,34(12):79-84.
[3] 钟先友,赵春华,陈保家,等.基于MCKD和重分配小波尺度谱的旋转机械复合故障诊断研究[J].振动与冲击,2015,34(7):156-161.
ZHONG Xian-you,ZHAO Chun-hua,CHEN Bao-jia,et al.Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram[J].Journal of Vibration and Shock,2015,34(7):156-161.
[4] Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London,1998,454(1971):903-995.
[5] Dragomiretskiy K,Zosso D.Variational mode decomposition [J].IEEE Transaction on Signal Processing,2014,62(3):531-544.
[6] LIU Y Y,YANG G L,LI M,et al.Variational mode decomposition denoising combined the detrended fluctuation analysis[J].Signal Processing,2016,125:349-364.
[7] WANG X D,ZI Y Y,HE Z J.Multiwavelet construction via an adaptive symmetric lifting scheme and its applications for rotating machinery fault diagnosis[J].Measurement Science and Technology,2009,20(4):1-17.
[8] Smith W A,Randall R B.Rolling element bearing diagnostics using the Case Western Reserve University data:A benchmark study[J].Mechanical Systems and Signal Processing,2015,64:100-131.

PDF(1342 KB)

Accesses

Citation

Detail

段落导航
相关文章

/