  Accurate diagnosis of propeller faults is crucial for ensuring the safe and
efficient operation of quadrotors. Training a fault classifier using simulated
data and deploying it on a real quadrotor is a cost-effective and safe
approach. However, the simulation-to-reality gap often leads to poor
performance of the classifier when applied in real flight. In this work, we
propose a deep learning model that addresses this issue by utilizing newly
identified features (NIF) as input and utilizing domain adaptation techniques
to reduce the simulation-to-reality gap. In addition, we introduce an adjusted
simulation model that generates training data that more accurately reflects the
behavior of real quadrotors. The experimental results demonstrate that our
proposed approach achieves an accuracy of 96\% in detecting propeller faults.
To the best of our knowledge, this is the first reliable and efficient method
for simulation-to-reality fault diagnosis of quadrotor propellers.
