The problem of the detection and isolation of input sensor faults on a general aviation aircraft, characterised by a nonlinear model, is addressed in the presence of wind gust disturbance and measurement errors. In particular, different residual generator designs are studied and compared in order to realise complete diagnosis schemes, when additive faults are present. These different methods comprise linear and nonlinear filters, neural networks and unknown input Kaiman filters, that can achieve disturbance signal decoupling and robustness properties with respect to modelling error and sensor measurement noise. The results obtained in the simulation of the faulty behaviour of a PIPER PA30 longitudinal aircraft model are finally reported.
Application of fault diagnosis methodologies to a general aviation aircraft
Anwendung von Fehlerdiagnosemethoden für ein Luftflugzeug
2006
6 Seiten, 2 Tabellen, 13 Quellen
Conference paper
English
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