  Monitoring propeller failures is vital to maintain the safe and reliable
operation of quadrotor UAVs. The simulation-to-reality UAV fault diagnosis
technique offer a secure and economical approach to identify faults in
propellers. However, classifiers trained with simulated data perform poorly in
real flights due to the wind disturbance in outdoor scenarios. In this work, we
propose an uncertainty-based fault classifier (UFC) to address the challenge of
sim-to-real UAV fault diagnosis in windy scenarios. It uses the ensemble of
difference-based deep convolutional neural networks (EDDCNN) to reduce model
variance and bias. Moreover, it employs an uncertainty-based decision framework
to filter out uncertain predictions. Experimental results demonstrate that the
UFC can achieve 100% fault-diagnosis accuracy with a data usage rate of 33.6%
in the windy outdoor scenario.
