Variational mode decomposition method and its application on incipient fault diagnosis of rolling bearing

被引：0
|
作者：
Tang G.-J. [1 ]
Wang X.-L. [1 ]
机构：
[1] School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding
来源：
Wang, Xiao-Long (wangxiaolong0312@126.com) | 1600年 / Nanjing University of Aeronautics an Astronautics卷 / 29期
关键词：
Equivalent filtering property; Fault diagnosis; Incipient fault; Rolling bearing; Variational mode decomposition;
D O I：
10.16385/j.cnki.issn.1004-4523.2016.04.011
中图分类号：
学科分类号：
摘要：
A adaptive signal decomposition method-variational mode decomposition was introduced, and aiming at solving the problem of incipient fault identification of rolling bearing, a diagnosis method based on VMD was proposed in this paper. Firstly, the equivalent filtering property of VMD was investigated via numerical simulation experiment based on fractional Gaussian noise and the division behaviour on frequency domain of wavelet packet-like was verified. Then the influence of the penalty factor and the number of component on the filtering property of VMD was researched. In order to reduce the drawback of subjectively selecting the influencing parameters in the process of bearing fault detecting, a strategy to automatically searching for the influencing parameters based on feature factor of envelope spectrum was proposed. Finally, the proposed method was verified through simulated signal and experimental signal. The results showed that this method could extract the weak feature information effectively and achieve accurate judgement of fault type. © 2016, Nanjing Univ. of Aeronautics an Astronautics. All right reserved.
引用
收藏
页码：638 / 648
页数：10
相关论文
共 17 条
  • [1] Leng Y., Zheng A., Fan S., SVD component-envelope detection method and its application in the incipient fault diagnosis of rolling bearing, Journal of Vibration Engineering, 27, 5, pp. 794-800, (2014)
  • [2] Ding K., Huang Z., Lin H., A weak fault diagnosis method for rolling element bearings based on morlet wavelet and spectral kurtosis, Journal of Vibration Engineering, 27, 1, pp. 128-135, (2014)
  • [3] Huang N.E., Shen Z., Long S.R., Et al., The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceeding of the Royal Society of London A, 454, pp. 903-995, (1998)
  • [4] Yang J., Zhao Y., Fault diagnosis of traction motor bearings using modified bispectrum and empirical mode decomposition, Proceedings of the CSEE, 32, 18, pp. 116-122, (2012)
  • [5] Yang Q., Chen G., He Q., Et al., Inchoate fault diagnosis of rolling bearings based on local tangent space alignment algorithm, Journal of Vibration, Measurement & Diagnosis, 32, 5, pp. 831-835, (2012)
  • [6] Zhu K., Song X., Xue D., Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient, Journal of Vibroengineering, 15, 2, pp. 597-603, (2013)
  • [7] Smith J.S., The local mean decomposition and its application to EEG perception data, Journal of the Royal Society Interface, 2, 5, pp. 443-454, (2005)
  • [8] Wang J., Wu L., Qin X., Et al., Rolling bearing vibration signal fault feature extraction based on autocorrelation analysis and LMD, China Mechanical Engineering, 25, 2, pp. 186-190, (2014)
  • [9] Chen B., He Z., Chen X., Et al., Locomotive fault diagnosis based on local mean decomposition demodulating approach, Journal of Xi'an Jiaotong University, 44, 5, pp. 40-44, (2010)
  • [10] Xu P., Zi Y., He Z., Weak feature extraction of spindle bearing fault with strong noise background for machine tool, Journal of Vibration and Shock, 29, pp. 121-123, (2010)
12