The main contribution of this paper is the description and application of a comprehensive set of methodologies for fault detection and isolation (FDI) of aircraft sensors. In particular, a new nonlinear geometric approach (NLGA) and an efficient linear polynomial method (PM) are presented and compared, together with simulation results obtained from a commercial aircraft model. Adaptive filters with disturbance decoupling for fault identification are designed via the developed NLGA-based method. On the other hand, the FDI scheme based on linear PM exploits a disturbance decoupling technique in connection with a linear dynamic filter design procedure. The FDI strategies are applied to the aircraft simulator data in a flight condition characterised by tight–coupled longitudinal and lateral dynamics. Moreover, in order to analyse robustness and reliability properties of the two FDI schemes, extensive simulations are performed in the presence of turbulence, measurement noise and modelling errors.

Design of residual generators and adaptive filters for the FDI of aircraft model sensors

CASTALDI, PAOLO;GERI, WALTER;
2010

Abstract

The main contribution of this paper is the description and application of a comprehensive set of methodologies for fault detection and isolation (FDI) of aircraft sensors. In particular, a new nonlinear geometric approach (NLGA) and an efficient linear polynomial method (PM) are presented and compared, together with simulation results obtained from a commercial aircraft model. Adaptive filters with disturbance decoupling for fault identification are designed via the developed NLGA-based method. On the other hand, the FDI scheme based on linear PM exploits a disturbance decoupling technique in connection with a linear dynamic filter design procedure. The FDI strategies are applied to the aircraft simulator data in a flight condition characterised by tight–coupled longitudinal and lateral dynamics. Moreover, in order to analyse robustness and reliability properties of the two FDI schemes, extensive simulations are performed in the presence of turbulence, measurement noise and modelling errors.
P. Castaldi; W. Geri; M. Bonfè; S. Simani; M. Benini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/92608
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