Failures of the air data system due to exceptional weather conditions have shown to be among the leading causes of aviation accidents. In this respect, most of aircraft Pitot tube failure detection schemes rely on mathematical models and simplified assumptions on the uncertain parameters and disturbances. These methods typically require 'ad hoc' time-consuming tuning procedures that may produce unreliable performance when validated with actual flight data. In this paper, a complete semiautomated data-driven approach is introduced to select the model regressors, to identify NARX input-output prediction models, to set up robust fault detection filters and to compute fault detection thresholds. To cope with time-dependent and flight-dependent levels of uncertainties online model adaption mechanisms are introduced to limit the critical problem of minimizing the false alarm rate. Extensive validation tests have been conducted using actual flight data of a P92 Tecnam aircraft through the introduction of artificially injected hard and soft failure of the Pitot tube sensor. The approach showed to be remarkably robust in terms of false alarms while maintaining fault detectability to faults of amplitudes less than 1 m/s.
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