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Identifying the fault in propellers is important to keep quadrotors operatingsafely and efficiently. The simulation-to-reality (sim-to-real) UAV faultdiagnosis methods provide a cost-effective and safe approach to detectingpropeller faults. However, due to the gap between simulation and reality,classifiers trained with simulated data usually underperform in real flights.In this work, a novel difference-based deep convolutional neural network(DDCNN) model is presented to address the above issue. It uses the differencefeatures extracted by deep convolutional neural networks to reduce thesim-to-real gap. Moreover, a new domain adaptation (DA) method is presented tofurther bring the distribution of the real-flight data closer to that of thesimulation data. The experimental results demonstrate that the DDCNN+DA modelcan increase the accuracy from 52.9% to 99.1% in real-world UAV faultdetection.

DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis