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On-Line Aircraft Engine Diagnostic Using a Soft-Constrained Kalman Filter

[+] Author Affiliations
P. Dewallef, O. Léonard

University of Liège, Liège, Belgium

K. Mathioudakis

National Technical University of Athens, Athens, Greece

Paper No. GT2004-53539, pp. 585-594; 10 pages
doi:10.1115/GT2004-53539
From:
  • ASME Turbo Expo 2004: Power for Land, Sea, and Air
  • Volume 2: Turbo Expo 2004
  • Vienna, Austria, June 14–17, 2004
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4167-7 | eISBN: 0-7918-3739-4
  • Copyright © 2004 by ASME

abstract

The purpose of this contribution is to apply ridge regression to Kalman filtering in order to stabilize a health parameter identification under low or negative redundancy. The resulting algorithm achieves a so-called soft-constrained recursive health parameter identification, i.e. constraints are applied to parameters in a statistical way, contrary to hard-constrained algorithms based on strong equality or inequality constrains. The method is tested on data generated by a steady state turbofan engine model and representing typical component faults. The benefits that can be realized in terms of stability and accuracy are highlighted and some limits of the method are also mentioned.

Copyright © 2004 by ASME

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