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Author:

Cui, L.-L. (Cui, L.-L..) (Scholars:崔玲丽) | Wu, C.-G. (Wu, C.-G..) | Wu, N. (Wu, N..)

Indexed by:

Scopus PKU CSCD

Abstract:

Aiming at the difficulty of separating rolling bearing composite faults in the case of single channel signal, a new algorithm based on empirical mode decomposition (EMD) and independent component analysis (ICA) was proposed in this paper. First, the composite bearing fault signals collected for a single channel were decomposed by EMD to obtain some intrinsic mode function (IMF). Then, main components were confirmed by calculating the correlation coefficient of every IMF and original composite signal and kurtosis value of every IMF. At the same time, main components with original signal were processed by ICA to realize the separation of composite fault of rolling bearings. Experimental results show that this method can effectively separate early rolling bearings composite fault.

Keyword:

Composite fault diagnosis of rolling bearings; Empirical mode decomposition (EMD); Independent component analysis (ICA); Kurtosis

Author Community:

  • [ 1 ] [Cui, L.-L.]Key Laboratory of Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Wu, C.-G.]Key Laboratory of Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Wu, N.]Key Laboratory of Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China

Reprint Author's Address:

  • 崔玲丽

    [Cui, L.-L.]Key Laboratory of Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of TechnologyChina

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2014

Issue: 10

Volume: 40

Page: 1459-1464

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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