|Reference : Comparison of adaptive filters for gas turbine performance monitoring|
|Scientific journals : Article|
|Physical, chemical, mathematical & earth Sciences : Physics|
Engineering, computing & technology : Materials science & engineering
Engineering, computing & technology : Mechanical engineering
Engineering, computing & technology : Multidisciplinary, general & others
|Comparison of adaptive filters for gas turbine performance monitoring|
|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 >]|
|Journal of Computational & Applied Mathematics|
|[en] Kalman filters ; gas path analysis ; adaptive estimation ; Generalised Likelihood Ratio ; covariance matching|
|[en] Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm has proven its capability to track gradual deterioration with a good accuracy. On the other hand, its response to rapid deterioration is either a long delay in recognizing the fault, and/or a spread of the estimated fault on several components.
The main reason of this deficiency lies in the transition model of the parameters that assumes a smooth evolution of the engine condition. The aim of this contribution is to compare two adaptive diagnosis tools that combine a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements on one hand a covariance matching scheme and on the other hand a generalised likelihood ratio test to improve the behaviour of the diagnosis tool with respect to abrupt faults.
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