Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by real flight data

• Proposed an approach for aircraft sensor fault estimation using a new kinematic model.
• Closed-loop and open-loop FDI validation strategies are proposed.
• A novel Adaptive Two-Stage Extended Kalman Filter is proposed.
• The proposed approach is robust to uncertainties and disturbances such as turbulence.
• The proposed approach is validated using real flight test data including turbulence.

This paper proposes an approach for Inertial Measurement Unit sensor fault reconstruction by exploiting a ground speed-based kinematic model of the aircraft flying in a rotating earth reference system. Two strategies for the validation of sensor fault reconstruction are presented: closed-loop validation and open-loop validation. Both strategies use the same kinematic model and a newly-developed Adaptive Two-Stage Extended Kalman Filter to estimate the states and faults of the aircraft. Simulation results demonstrate the effectiveness of the proposed approach compared to an approach using an airspeed-based kinematic model. Furthermore, the major contribution is that the proposed approach is validated using real flight test data including the presence of external disturbances such as turbulence. Three flight scenarios are selected to test the performance of the proposed approach. It is shown that the proposed approach is robust to model uncertainties, unmodeled dynamics and disturbances such as time-varying wind and turbulence. Therefore, the proposed approach can be incorporated into aircraft Fault Detection and Isolation systems to enhance the performance of the aircraft.