Volume 26 Issue 12
Dec.  2011
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Article Navigation >  Journal of Aerospace Power  > 2011  >  26(12): 2841-2848
XU Qi-hua, GENG Shuai, SHI Jun. Fault diagnosis method for aero-engine based on SVM with large-scale training set[J]. Journal of Aerospace Power, 2011, 26(12): 2841-2848.
Citation: XU Qi-hua, GENG Shuai, SHI Jun. Fault diagnosis method for aero-engine based on SVM with large-scale training set[J]. Journal of Aerospace Power, 2011, 26(12): 2841-2848.
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Fault diagnosis method for aero-engine based on SVM with large-scale training set

  • 1.

    College of Electronic Engineering, Huaihai Institute of Technology, Lianyungang Jiangsu 222005, China

  • 2.

    No.365 Institute, Northwestern Polytechnical University, Xi'an 710072, China

  • Received Date: 2011-08-08
  • Rev Recd Date: 2011-11-18
  • Publish Date: 2011-12-28
    Abstract
  • A learning strategy was presented to solve the large-scale support vector machines (SVM) training problem of aero-engine fault diagnosis.According to the strategy,only those samples were retained for final training,which were near the support vector hyperplane of the original small-scale SVM classifier or classified mistakenly by the original classifier.The final pruning set was reduced evidently and the training time was shortened obviously on the condition of high classification accuracy.Meanwhile,the classification time was shortened correspondingly because of the decrease of the support vector number.Something about the parameter selection of the sequential minimal optimization (SMO) method and how to use this potential algorithm in aero-engine fault diagnosis were also discussed.Simulation examples show that this fault diagnosis method proposed for aero-engine,based on SVM with large-scale training set,is effective,reliable and easy to be implemented for engineering application.

     

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