Article (Scientific journals)
Combining classification techniques with Kalman filters for aircraft engine diagnostics
Dewallef, Pierre; Romessis, C.; Léonard, Olivier et al.
2006In Journal of Engineering for Gas Turbines and Power, 128 (2), p. 281-287
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Abstract :
[en] A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Network (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 hers improved identification capability in comparison to the stand-alone Kalman filter. The paper focuses on a 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 presented.
Disciplines :
Mechanical engineering
Author, co-author :
Dewallef, Pierre ;  Université de Liège - ULiège > Département d'aérospatiale et mécanique > Systèmes de conversion d'énergie pour un dévelop.durable
Romessis, C.
Léonard, Olivier ;  Université de Liège - ULiège > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale
Mathioudakis, K.
Language :
English
Title :
Combining classification techniques with Kalman filters for aircraft engine diagnostics
Publication date :
April 2006
Journal title :
Journal of Engineering for Gas Turbines and Power
ISSN :
0742-4795
eISSN :
1528-8919
Publisher :
American Society of Mechanical Engineers, New York, United States - New York
Volume :
128
Issue :
2
Pages :
281-287
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 26 August 2009

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