Reference : A way to deal with model-plant mismatch for a reliable diagnosis in transient operation
Scientific journals : Article
Engineering, computing & technology : Aerospace & aeronautics engineering
Physical, chemical, mathematical & earth Sciences : Space science, astronomy & astrophysics
Physical, chemical, mathematical & earth Sciences : Physics
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/2268/19951
A way to deal with model-plant mismatch for a reliable diagnosis in transient operation
English
Borguet, Sébastien mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]
Dewallef, Pierre mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > Systèmes de conversion d'énergie pour un dévelop.durable >]
Léonard, Olivier mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]
May-2008
Journal of Engineering for Gas Turbines & Power
American Society of Mechanical Engineers
130
3
Yes (verified by ORBi)
International
0742-4795
New York
NY
[en] condition monitoring ; engine health monitoring ; Kalman filters ; least squares approximations ; turbomachinery ; model-plant mismatch
[en] Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction and the measurements are Zero-mean, white, Gaussian random variables. In a turbine engine diagnosis, however this assumption does not always hold due to the presence of biases in the model. This is especially true for a transient operation. As a result, the estimated parameters tend to diverge from their actual values, which strongly degrades the diagnosis. The purpose of this contribution is to present a Kalman filter diagnosis tool where the model biases are treated as an additional random measurement error. The new methodology is tested on simulated transient data representative of a current turbofan engine configuration. While relatively simple to implement, the newly developed diagnosis tool exhibits a much better accuracy than the original Kalman filter in the presence of model biases.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/19951
10.1115/1.2833491

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