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.
Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics
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Dewallef, P., Romessis, C., Léonard, O., and Mathioudakis, K. (March 1, 2004). "Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics." ASME. J. Eng. Gas Turbines Power. April 2006; 128(2): 281–287. https://doi.org/10.1115/1.2056507
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