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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2008  >  8(5): 33-37
WANG Xu-hui, HUANG Sheng-guo, SHI Ding-hao, SHU Ping. Gas path fault prognosis of aeroengine based on support vector machine[J]. Journal of Traffic and Transportation Engineering, 2008, 8(5): 33-37.
Citation: WANG Xu-hui, HUANG Sheng-guo, SHI Ding-hao, SHU Ping. Gas path fault prognosis of aeroengine based on support vector machine[J]. Journal of Traffic and Transportation Engineering, 2008, 8(5): 33-37.
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Gas path fault prognosis of aeroengine based on support vector machine

  • 1.

    School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China

  • 2.

    Center of Aviation Safety Technology, Civil Aviation Administration of China, Beijing 100028, China

More Information
  • Author Bio:

    WANG Xu-hui (1979-), male, doctoral student, +86-25-84892273, wxhui@nuaa.edu.cn

    HUANG Sheng-guo (1941-), male, professor, +86-25-84892273, huangsg@nuaa.edu.cn

  • Received Date: 2008-04-25
  • Publish Date: 2008-10-25
    Abstract
  • In order to reality the online forecast of aeroengine gas path fault, the protocol and content of the gas path parameters' report in aircraft communication addressing and reporting system(ACARS) were analyzed, and an online forecast model of the parameters based on support vector machine(SVM) algorithm was established.A real-time processing system of aeroengine report was built by using portable air-ground data link transceiver for obtaining the training samples of the model.Final prediction error(FPE) principle was suggested to optimize the embedding dimensions of the samples, and the phase spaces of the samples were reconstructed.An adaptive grid search algorithm was put forward to optimize the parameters of the model, and the model was linked to route plane by using ACARS.Forecast result shows that the relative forecast errors of low-pressure compressor rotor speed, high-pressure compressor rotor speed, exhaust gas temperature and fuel flow are 2.5%, 2.1%, 1.9% and 2.3% respectively, so the model is feasible.

     

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