Reference : A Quadratic Programming Framework for Constrained and Robust Jet Engine Health Monitiring

	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Aerospace & aeronautics engineering Physical, chemical, mathematical & earth Sciences : Mathematics
	To cite this reference:	http://hdl.handle.net/2268/20880

Title :	A Quadratic Programming Framework for Constrained and Robust Jet Engine Health Monitiring
Language :	English
Author, co-author :	Borguet, Sébastien [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]
	Léonard, Olivier [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]
Publication date :	2007
Main document title :	Proceedings of the 2nd European Conference on Aerospace Sciences
Audience :	International
Event name :	2nd European Conference on Aerospace Sciences
Event place (city) :	Brussels
Event country :	Belgium
Keywords :	[en] Quadratic Programming ; jet engine community ; monitoring purpose ; robustness ; turbofan engines
Abstract :	[en] Kalman filters are largely used in the jet engine community for condition monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the residuals between the model prediction and the measurements are zero-mean, Gaussian random variables. In the case of sensor faults, this assumption does not hold anymore and consequently the diagnosis is spoiled. This contribution presents a recursive estimation algorithm based on a Quadratic Programming formulation which provides robustness against sensor faults and allows constraints on the health parameters to be specified. The improvements in estimation accuracy brought by this new algorithm are illustrated by a series of typical test-cases that may be encountered on current turbofan engines.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/20880

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