Fault Diagnosis of Rolling Element Bearings based on EMD and MKD
SUI Wen-tao 1 ZHANG Dan 2 Wilson Wang 3
1. School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China；
2. School of Electrical & Electronic Engineering, Shandong University of Technology, Zibo 255049, China；
3. Dept. of Mechanical Engineering, Lakehead University , Thunder Bay, ON, Canada P7B 5E1,Canada
Abstract： Aiming to the difficulty in feature extraction of early fault for rolling element bearings, the method based on Empirical Mode Decomposition (EMD) and Maximum Kurtosis Deconvolution (MKD) was proposed to extract the feature. The vibration signal was decomposed into a group of Intrinsic Mode Functions (IMF) through EMD. According to the kurtosis of time-domain and envelope spectrum, the sensitive IMF component was selected and reconstructed into a new signal. The reconstructed signal was processed by using maximum kurtosis deconvolution to enhance the fault information. Finally, the envelope power spectrum was obtained to analyze the bearing fault characteristic frequency information. The effectiveness and advantages of the proposed method were proved through the the signals collected from test rig.
隋文涛1,张丹2， Wilson Wang3. 基于EMD和MKD的滚动轴承故障诊断方法[J]. 振动与冲击, 2015, 34(9): 55-59.
SUI Wen-tao 1 ZHANG Dan 2 Wilson Wang 3 . Fault Diagnosis of Rolling Element Bearings based on EMD and MKD. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(9): 55-59.
[1] 周智，朱永生，张优云等．基于MMSE和谱峭度的滚动轴承故障诊断方法[J]．振动与冲击，2013，32 (6): 73-77.
ZHOU Zhi ,ZHU Yong-sheng,ZHANG You-yun．Fault diagnosis method for rolling bearings based on MMSE and spectral kurtosis [J]．Journal of Vibration and Shock, 2013,32(6): 73-77.
[2] 唐宏宾，吴运新，滑广军等．基于EMD包络谱分析的液压泵故障诊断方法[J]。振动与冲击，2012，31(9)：44-48．
TANG Hong-bin ，WU Yun-xin，HUA Guang-jun．Fault diagnosis of pump using EMD and envelope spectrum analysis [J]．Journal of Vibration and Shock, 2012，31(9)：44-48.
[3] 苏文胜，王奉涛，张志新等．EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]。振动与冲击，2010，29(3)：18-21．
Su Wensheng, Wang Fengtao,Zhang Zhiin．Application of EMD Denoising and Spectral Kurtosis in Fault Early Diagnosis of Rolling Element Bearing [J]．Journal of Vibration and Shock, 2010，29(3)：18-21.
[4] 张志刚，石晓辉，陈哲明，汤宝平．基于改进EMD与滑动峰态算法的滚动轴承故障特征提取[J]。振动与冲击，2012，31(22)：80-83．
ZHANG Zhi-gang，SHI Xiao-hui，CHEN Zhe-ming，TANG Bao-ping．Fault feature extraction of rolling element bearing based on improved EMD and sliding kurtosis algorithm [J]．Journal of Vibration and Shock, 2012，31(22)：80-83.
[5] 雷亚国．基于改进HilbertHuang变换的机械故障诊断[J]。机械工程学报，2011，47(5)：71-77
LEI Yaguo．Machinery Fault Diagnosis Based on ImproVed Hilbert•Huang Transform [J]．Journal of Mechanical Engineering, 2011，47(5)：71-77.
[6] N.E.Huang, Z.Shen, S.R.Long,etal．The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc.R.Soc. London A 454 (1998) 903–995.
[7] Ralph, A.W ．Minimum entropy deconvolution [J]．Geoexploration，1978,16(1)：21-35.
[8] Endo H., Randall R.B. Application of a minimum entropy deconvolution filter to enhance Autoregressive model based gear tooth fault detection technique [J]. Mechanical Systems and Signal Processing, 2007,21：906–919.
[9] Sawalhi, N., Randall, R. B. & Endo, H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis [J]. Mechanical Systems and Signal Processing，2007,21：2616-2633.
[10] W. Wang and H. Lee．An energy kurtosis demodulation technique for signal denoising and bearing fault detection [J]．Measurement Sciences and Technology，2013，24：1-11.
[11] 王宏超，陈 进，董广明．基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J]. 机械工程学报，2013，49(1)：88-94.
WANG Hongchao, CHEN Jin, DONG, Guangming．Fault Diagnosis Method for Rolling Bearing's Weak Fault Based on Minimum Entropy Deconvolution and Sparse Decomposition [J]．Journal of mechanical engineering, 2013，49(1)：88-94.
[12] K.A. Loparo, Bearings vibration data set, http://csegroups.case.edu/bearingdatacenter/.
