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Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics

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

University of Liège, Liège, Belgium

C. Romessis, K. Mathioudakis

National Technical University of Athens, Athens, Greece

Paper No. GT2004-53541, pp. 595-603; 9 pages
doi:10.1115/GT2004-53541
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

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Networks (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand alone Kalman filter. The paper focuses on the way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated and its advantages over individual constituent methods are shown.

Copyright © 2004 by ASME

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