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Volume 38 Issue 11
Nov.  2012
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Journal of Beijing University of Aeronautics and Astronautics  > 2012  >  (11): 1557-1561.
Yang Yanlong, Cheng Wei, Chang Hongzhenet al. Fault diagnosis based on blind source separation using kernel function with finite support samples[J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, (11): 1557-1561. (in Chinese)
Citation: Yang Yanlong, Cheng Wei, Chang Hongzhenet al. Fault diagnosis based on blind source separation using kernel function with finite support samples[J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, (11): 1557-1561. (in Chinese)
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Fault diagnosis based on blind source separation using kernel function with finite support samples

  • School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 28 Jul 2011
  • Publish Date: 30 Nov 2012
    Abstract
  • It is important to extract fault features when machine would be in fault state. In order to separate different fault vibration signals from measured mixtures and diagnose the fault features of the machine effectively according to the separated signals, a blind source separation (BSS) method using kernel function based on finite support samples was proposed. The method is stronger adaptability for the score functions estimated according to finite support observed signal samples. The simulation results prove that the proposed BSS algorithm is able to separate hybrid mixtures that contain both sub-gaussian and super-gaussian sources. It is shown that the algorithm has better separation performance when compared with other BSS ones. The results of an experiment under the motor’s composite fault states with pedestal looseness fault and rotor unbalance fault show that this method is feasible for fault diagnosis.

     

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