EEMD method based singular value spectral entropy in fault diagnosis of rotating machinery

被引：5
|
作者：
Gao Q. [1 ]
Jia M. [1 ]
机构：
[1] School of Mechanical Engineering, Southeast University
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2011年 / 41卷 / 05期
关键词：
Chatter; Ensemble empirical mode decomposition; Rotating machinery; Singular value spectral entropy;
D O I：
10.3969/j.issn.1001-0505.2011.05.020
中图分类号：
学科分类号：
摘要：
For the non-stationary and non-linear characteristics of rotating machinery vibration signal, ensemble empirical mode decomposition (EEMD) method based singular value spectral entropy is proposed for signal analysis and fault diagnosis of rotating machinery. This method utilizes the advantage of EEMD which can effectively restrain model mixing. First, the EEMD method is used to decompose the original signal to obtain intrinsic mode functions (IMFs). Then, a feature pattern matrix is created by intrinsic mode functions. Finally, the singular spectrum entropy of the feature pattern matrix is calculated, since singular spectrum entropy can reflect the system's working condition and fault type. Singular value spectral entropy based on the EMD method and the EEMD method are respectively used to analyze and compare the turning chatter vibration signals. The result verifies that the proposed method is effective and feasible.
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页码：998 / 1001
页数：3
相关论文
共 10 条
  • [1] (2005)
  • [2] Lei Y., He Z., Zi Y., Application of the EEMD method to rotor fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 23, 4, pp. 1327-1338, (2009)
  • [3] Jia M., Ling J., Xu F., Et al., Empirical mode decomposition based on time series analysis and its application, Chinese Journal of Mechanical Engineering, 40, 9, pp. 54-57, (2004)
  • [4] Ai S., Li H., Gear fault detection based on ensemble empirical mode decomposition and Hilbert-Huang transform, Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 173-177, (2008)
  • [5] Zhang J., Yan R., Gao R.X., Ensemble empirical mode decomposition for machine health diagnosis, Key Engineering Materials, 413-414, pp. 167-174, (2009)
  • [6] Zhang L., Luo S., Xiao Y., Et al., Application of EEMD in fault diagnosis of diesel engine, Science Technology and Engineering, 10, 27, pp. 6745-6749, (2010)
  • [7] Huang N.E., Wu Z.H., Ensemble empirical mode decomposition: A noise-assisted data analysis method, World Scientific, 1, 1, pp. 1-41, (2009)
  • [8] Yu D., Chen M., Cheng J., Et al., Fault diagnosis approach for rotor system based on EMD method and singular value entropy, Journal of Vibration and Shock, 25, 2, pp. 24-26, (2006)
  • [9] Wu Z.H., Huang N.E., A study of the characteristics of white noise using the empirical mode decomposition method, Proceedings of the Royal Society, 460, 2046, pp. 1597-1611, (2004)
  • [10] Chen F., Huang S., Zhang Y., Et al., Research on diagnosis of vibration faults rotating machinery based on distance of information entropy, Journal of Vibration, Measurement and Diagnosis, 28, 1, pp. 9-13, (2008)
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