Volume 26 Issue 11
Nov.  2011
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WU Ya-hui, LI Xin-liang, HONG Bao-lin, ZHANG Da-zhi. Research on aeroengine vibration fault diagnosis based on support vector machine and generalized roughness feature[J]. Journal of Aerospace Power, 2011, 26(11): 2445-2449.
Citation: WU Ya-hui, LI Xin-liang, HONG Bao-lin, ZHANG Da-zhi. Research on aeroengine vibration fault diagnosis based on support vector machine and generalized roughness feature[J]. Journal of Aerospace Power, 2011, 26(11): 2445-2449.

Research on aeroengine vibration fault diagnosis based on support vector machine and generalized roughness feature

  • Received Date: 2011-04-08
  • Rev Recd Date: 2011-09-20
  • Publish Date: 2011-11-28
  • The signal was decomposed based on wavelet transform and the generalized roughness vector of the signal was formed by making use of the local energy distributions and the roughness of the sub-band signal.Then the desired parameters serve as the fault characteristic vectors to be input to the support vector machine classifier and the work conditions and fault patterns were identified by the output of the classifier.The analysis results from the aeroengine vibration signals show that the fault diagnosis method can classify working conditions and fault patterns effectively.

     

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