Modeling a Complex Aero-Engine Using Reduced Order Models

  • Xuewu DaiEmail author
  • Timofei Breikin
  • Hong Wang
  • Gennady Kulikov
  • Valentin Arkov


Gas turbine engines are widely used in many industrial applications and engine condition monitoring is a vital issue for the aircraft in-service use and flight safety. From the variety of condition monitoring methods, the model-based approach is perhaps the most promising for real-time condition monitoring. This approach can predict the engine characteristics at the expense of Ȝalgorithmic redundancyȝ and requires real-time simulation. The main obstacles for using full thermodynamic models in the engine condition monitoring schemes are high computing load, and inability to incorporate unforeseen changes.


Condition Monitoring Equation Error Reduce Order Model Output Error Actuator Fault 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    B. Anderson and C. Johnson, Jr. On reduced-order adaptive output error identification and adaptive IIR filtering. IEEE Transactions on Automatic Control, 27(4):927–933, 1982CrossRefMathSciNetGoogle Scholar
  2. 2.
    K. J. Astrom and P. Eykhoff. System identification – a survey. Automatica, 7(2):123–162, March 1971CrossRefMathSciNetGoogle Scholar
  3. 3.
    T.V. Breikin, G. G. Kulikov, V. Y. Arkov, and Peter. J. Fleming. Dynamic modelling for condition monitoring of gas turbines: Genetic algorithms approach. In Proceedings of 16th IFAC World Congress, 2005Google Scholar
  4. 4.
    X. Dai, T. Breikin, and H. Wang. An algorithm for identification of reduced-order dynamic models of gas turbines. In Proceedings of 1st International Conference on Innovative Computing, Information and Control, volume 1, pages 134–137, 2006Google Scholar
  5. 5.
    C. Evans. Testing and modelling aircraft gas turbines: An introduction and overview. In UKACC International Conference on Control’98, pages 1361–1366, 1998CrossRefGoogle Scholar
  6. 6.
    R. Fletcher. A new approach to variable metric algorithms. The Computer Journal, 13: 317–322, 1970zbMATHCrossRefGoogle Scholar
  7. 7.
    M. Hong, T. Soderstrom, and W. X. Zheng. A simplified form of the bias-eliminating least squares method for errors-in-variables identification. IEEE Transactions on Automatic Control, 52(9):1754–1756, September 2007CrossRefMathSciNetGoogle Scholar
  8. 8.
    R. Isermann. Model-based fault detection and diagnosis – status and applications. In Proceeding of 16th IFAC Symposium on Automatic Control in Aerospace, 2004Google Scholar
  9. 9.
    J.-S.R. Jang, C.-T. Sun, and E. Mizutani. Neuro-Fuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs, 1997Google Scholar
  10. 10.
    D. Kahaner, C. B. Moler, and S. Nash. Numerical Methods and Software. Prentice-Hall, Englewood Cliffs, 1989zbMATHGoogle Scholar
  11. 11.
    G. G. Kulikov and H. A. Thompson. Dynamic Modelling of Gas Trubines: Identification, Simulation, Condition Monitoring and Optimal Control. Springer, London, 2004Google Scholar
  12. 12.
    I. Landau. Unbiased recursive identification using model reference adaptive techniques. IEEE Transactions on Automatic Control, 21(2):194–202, April 1976zbMATHCrossRefGoogle Scholar
  13. 13.
    L. Ljung. System Identification: Theory for the User. Prentice Hall, London, 2nd edition, 1999Google Scholar
  14. 14.
    S. L. Netto, P. S. R. Diniz, and P. Agathoklis. Adaptive IIR filtering algorithms for system identification: A general framework. IEEE Transactions on Education, 38(1):54–66, February 1995CrossRefGoogle Scholar
  15. 15.
    A. Wills and B. Ninness. On gradient-based search for multivariable system estimates. IEEE Transactions on Automatic Control, 53:298–306, 2008CrossRefMathSciNetGoogle Scholar
  16. 16.
    P. C. Young. Recursive Estimation and Time-Series Analysis. Springer, Berlin, 1984zbMATHGoogle Scholar
  17. 17.
    P. C. Young, H. Garnier, and M. Gilson. An optimal instrumental variable approach for identifying hybrid Box-Jenkins models. In 14th IFAC Symposium on System Identification SYSID06, pages 225–230, Newcastle, NSW, Australia, 2006Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Xuewu Dai
    • 1
    • 2
    Email author
  • Timofei Breikin
    • 2
  • Hong Wang
    • 2
  • Gennady Kulikov
    • 3
  • Valentin Arkov
    • 3
  1. 1.School of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.Control Systems Centre, School of of Electrical and Electronic EngineeringUniversity of ManchesterManchesterUK
  3. 3.Department of Automated Control SystemsUfa State Aviation Technical UniversityUfaRussia

Personalised recommendations