Fault diagnosis of rolling element bearings based on EMD and MKD

被引：0
|
作者：
Sui, Wen-Tao [1 ]
Zhang, Dan [2 ]
Wang, Wilson [3 ]
机构：
[1] School of Mechanical Engineering, Shandong University of Technology, Zibo
[2] School of Electrical & Electronic Engineering, Shandong University of Technology, Zibo
[3] Dept. of Mechanical Engineering, Lakehead University, Thunder Bay, P7B 5E1, ON
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2015年 / 34卷 / 09期
关键词：
Empirical mode decomposition; Fault diagnosis; Maximum kurtosis deconvolution; Rolling element bearings;
D O I：
10.13465/j.cnki.jvs.2015.09.010
中图分类号：
学科分类号：
摘要：
Aiming at the difficulty in feature extraction of early faults for rolling element bearings, the method based on Empirical Mode Decomposition (EMD) and Maximum Kurtosis Deconvolution (MKD) was proposed to extract features. The vibration signal was decomposed into a group of Intrinsic Mode Functions (IMF) through EMD. According to the kurtosises of time-domain signal and of envelope spectrum, the sensitive IMF components were selected and reconstructed into a new signal. The reconstructed signal was processed by using MKD 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 by processing the signals collected from test rig. ©, 2015, Chinese Vibration Engineering Society. All right reserved.
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页码：55 / 59and64
页数：5909
相关论文
共 12 条
  • [1] Zhou Z., Zhu Y.-S., Zhang Y.-Y., Et al., Fault diagnosis method for rolling bearings based on MMSE and spectral kurtosis, Journal of Vibration and Shock, 32, 6, pp. 73-77, (2013)
  • [2] Tang H.-B., Wu Y.-X., Hua G.-J., Et al., Fault diagnosis of pump using EMD and envelope spectrum analysis, Journal of Vibration and Shock, 31, 9, pp. 44-48, (2012)
  • [3] Su W.-S., Wang F.-T., Zhang Z.-X., Application of EMD Denoising and spectral kurtosis in fault early diagnosis of rolling element bearing, Journal of Vibration and Shock, 29, 3, pp. 18-21, (2010)
  • [4] Zhang Z.-G., Shi X.-H., Chen Z.-M., Et al., Fault feature extraction of rolling element bearing based on improved EMD and sliding kurtosis algorithm, Journal of Vibration and Shock, 31, 22, pp. 80-83, (2012)
  • [5] Lei Y.-G., Machinery fault diagnosis based on improved Hilbert·Huang transform, Journal of Mechanical Engineering, 47, 5, pp. 71-77, (2011)
  • [6] Huang N.H., Shen Z., Long S.R., Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London A, 454, pp. 903-995, (1998)
  • [7] Ralph A.W., Minimum entropy deconvolution, Geoexploration, 16, 1, pp. 21-35, (1978)
  • [8] Endo H., Randall R.B., Application of a minimum entropy deconvolution filter to enhance Autoregressive model based gear tooth fault detection technique, Mechanical Systems and Signal Processing, 21, pp. 906-919, (2007)
  • [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, Mechanical Systems and Signal Processing, 21, pp. 2616-2633, (2007)
  • [10] Wang W., Lee H., An energy kurtosis demodulation technique for signal denoising and bearing fault detection, Measurement Sciences and Technology, 24, pp. 1-11, (2013)
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