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Several procedures for sensor fault detection and isolation (FDI) applied to a simulated model of a commercial aircraft are presented. The main contributions of the paper are related to the design and the optimisation of two FDI schemes based on a linear polynomial method (PM) and the nonlinear geometric approach (NLGA). The FDI strategies are applied to the aircraft model, characterised by tight-coupled longitudinal and lateral dynamics. The robustness and the reliability properties of the residual generators related to the considered FDI techniques are investigated and verified by simulating a general aircraft reference trajectory. Extensive simulations exploiting the Monte Carlo analysis tool are also used for assessing the overall performance capabilities of the developed FDI schemes, in the presence of turbulence, measurement, and model errors. Comparisons with other disturbance-decoupling methods for FDI based on neural networks (NNs) and unknown input kalman filter (UIKF) are finally reported.

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This paper addresses the problem of the detection and isolation of the input and output sensor faults for a linear multivariable sampled-data dynamic system, in the presence of disturbance signals. In particular, this work proposes a polynomial approach for the design of residual generators in order to realise a complete diagnosis scheme when additive faults are present. It is shown that the use of an inputoutput description for the linear dynamic sampled-data model of the system under investigation allows to compute in a straightforward way the discrete-time residual generators. The residual generator design is performed in order to maximise a suitable fault sensitivity function. Thus, the suggested design approach leads to dynamic filters that achieve both disturbance de-coupling and desired transient properties in terms of a fault to residual reference transfer function. The results obtained in the simulation of the faulty behaviour of a discrete-time turbine jet engine model are finally reported.

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