Aeroengine Turbine Exhaust Gas Temperature Prediction Using Support Vector Machines

  • Xuyun Fu
  • Gang Ding
  • Shisheng Zhong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)


The turbine exhaust gas temperature (EGT) is an important parameter of the aeroengine and it represents the thermal health condition of the aeroengine. By predicting the EGT, the performance deterioration of the aeroengine can be deduced in advance. Thus, the flight safety and the economy of the airlines can be guaranteed. However, the EGT is influenced by many complicated factors during the practical operation of the aeroengine. It is difficult to predict the change tendency of the EGT effectively by the traditional methods. To solve this problem, a novel EGT prediction method based on the support vector machines (SVM) is proposed. Finally, the proposed prediction method is utilized to predict the EGT of some aeroengine, and the results are satisfying.


Aeroengine condition monitoring Turbine exhaust gas temperature Support vector machines Time series prediction 


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  1. 1.
    Rong, X., Zuo, H.F., Zhang, H.J.: Control Method of Time on Wing for Civil Aeroengine. Journal of Traffic and Transportation Engineering 8(3), 28–32 (2008)Google Scholar
  2. 2.
    Ma, N.C., Jia, X.J., Zhu, H.Y.: Development of Aviation Materiel Maintenance Ideology. Aeronautical Science and Technology 6, 27–29 (2003)Google Scholar
  3. 3.
    Chatfield, C.: The Analysis of Time Series: An Introduction, 6th edn. Chapman & Hall/CRC, Boca Raton (2003)zbMATHGoogle Scholar
  4. 4.
    Wang, G., Li, J.: Aeroengine Lube Monitoring Analysis based on Neutral Net. Lubrication Engineering 5, 123–125 (2005)Google Scholar
  5. 5.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks Are Universal Approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  6. 6.
    Funahashi, K.: On The Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks 2, 183–192 (1989)CrossRefGoogle Scholar
  7. 7.
    Vassilopoulos, A.P., Georgopoulos, E.F., Dionysopoulos, V.: Artificial Neural Networks in Spectrum Fatigue Life Prediction of Composite Materials. International Journal of Fatigue 29, 20–29 (2007)CrossRefGoogle Scholar
  8. 8.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xuyun Fu
    • 1
  • Gang Ding
    • 1
  • Shisheng Zhong
    • 1
  1. 1.School of Mechatronics EngineeringHarbin Institute of TechnologyHarbinChina

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