An Immune Neural Network Model for Aeroengine Performance Monitoring

  • Wei WangEmail author
  • Shengli Hou
  • Jing Guo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)


In this paper, an aeroengine performance monitoring and fault detection model, based on immune neural network, is put forward. By combining artificial immune system recognition mechanism with artificial neural network, the deviation degree of aeroengine performance (abnormal degree) can be determined, and the monitoring of performance trend can be achieved. With this method, the overall performance change of aeroengine can be reflected sensitively and accurately, the abnormity recognition rate of aeroengine performance can be enhanced, and potential early engine fault can be detected to prevent further development. This method is proved effective through the monitoring of a certain type of turbofan aeroengine.


Aerospace propulsion system Aeroengine Performance monitoring Artificial immune system Immune neural network 


  1. 1.
    Hu, J., Xie, S.: Performance monitoring and fault diagnosis of engines based genetic algorithm. J. Propul. Technol. 24(3), 198–200 (2003)Google Scholar
  2. 2.
    Hou, S., Hu, J., Li, Y.: Aeroengine performance monitoring and fault diagnosis based on chaos variable. J. Aerospace Power 20(2), 314–317 (2005)Google Scholar
  3. 3.
    Hou, S., Wang, W., Qiao, L.: Feature extraction and multi-sensor fault diagnosis based on clonal clustering. J. Electron. Optics Control 17(6), 69–72 (2010)Google Scholar
  4. 4.
    Hou, S., Wang, W., Hu, J.: Neural network-based immune recognition model for aero-engine surge detection. J. Vibr. Shock 29(1), 170–172 (2010)Google Scholar
  5. 5.
    Gonzalez, F., Dasgupta, D., Kozma, R.: Combining negative selection and classification techniques for anomaly detection. In: Proceedings of the Congress on Evolutionary Computation, pp. 705–710. Hawaii (2012)Google Scholar
  6. 6.
    Esponda, F., Forrest, S., Helman, P.: A formal framework for positive and negative detection schemes. J. IEEE Trans. Syst. Man Cybern. B, Cybern 34(1), 357–373 (2014)CrossRefGoogle Scholar
  7. 7.
    Wei, X., Feng, Y., Liu, F.: Development strategy and key prognostics health management technologies for military aero-engine in China. J. Aerospace Power 26(9), 2107–2115 (2011)Google Scholar
  8. 8.
    Roemer, M.J., Nwadiogbu, E.O.: Development of diagnostic and prognostic technologies for aerospace health management applications. J. Paper 2001-GT-30, ASME and IGTI Turbo Expo 2012, Munich, Germany (2012)Google Scholar
  9. 9.
    Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Aviation Material ManagementAir Force Logistics CollegeXuzhouChina

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