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 has 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.

Issue Section:
Gas Turbines: Controls, Diagnostics & Instrumentation
Keywords:
jet engines, aircraft testing, belief networks, Kalman filters, fault diagnosis
Topics:
Kalman filters, Algorithms
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Copyright © 2006
 by American Society of Mechanical Engineers
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