Detection and Isolation of Propeller Icing and Electric Propulsion System Faults in Fixed-Wing UAVs

Fault in the propulsion system of UAVs is one of the main causes for incidents and loss of the aircraft. In electric propulsion systems typical faults are ball-bearing degradation leading to increased friction and losses, as well as propeller icing when operating in cold conditions. In this paper we propose a fault detection and isolation (FDI) framework that uses model-based estimators of the various faults, implemented with multiple Kalman and Bayes filters. The method is tested and shown to be effective in a simulation setup using a fixed-wing UAV simulator.