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Interval Fuzzy Model For Robust Aircraft Imu Sensors Fault Detection, Michele Crispoltoni, Mario Luca Fravolini, Fabio Balzano, Stephane D'Urso, Marcello Rosario Napolitano

Faculty & Staff Scholarship

This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs are used to compute robust prediction intervals for the measurements provided by the IMU sensors. Specifically, a nonlinear neural network (NN) model is used as central prediction of the sensor response while the uncertainty around the central estimation is captured by the FIM model. The uncertainty has been also modelled using a conventional linear Interval Model …