Rolling Bearings Fault Diagnosis Based on Adaptive Gaussian Chirplet Spectrogram and Independent Component Analysis

  • Haibin Yu
  • Qianjin Guo
  • Jingtao Hu
  • Aidong Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


Condition monitoring of rolling element bearings through the use of vibration analysis is an established technique for detecting early stages of component degradation. The location dependent characteristic defect frequencies make it possible to detect the presence of a defect and to diagnose on what part of the bearing the defect is. The difficulty of localized defect detection lies in the fact that the energy of the signature of a defective bearing is spread across a wide frequency band and hence can be easily buried by noise. To solve this problem, the adaptive Gaussian chirplet distribution for an integrated time-frequency signature extraction of the machine vibration is developed; the method offers the advantage of good localization of the vibration signal energy in the time-frequency domain. Independent component analysis (ICA) is used for the redundancy reduction and feature extraction in the time-frequency domain, and the self-organizing map (SOM) was employed to identify the faults of the rolling element bearings. Experimental results show that the proposed method is very effective.


Independent Component Analysis Fault Diagnosis Vibration Signal Independent Component Analysis Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Haibin Yu
    • 1
  • Qianjin Guo
    • 1
    • 2
  • Jingtao Hu
    • 1
  • Aidong Xu
    • 1
  1. 1.Shenyang Inst. of AutomationChinese Academy of SciencesLiaoningChina
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina

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