Identifying the fault in propellers is important to keep quadrotors operating safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault diagnosis methods provide a cost-effective and safe approach to detecting propeller 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 difference features extracted by deep convolutional neural networks to reduce the sim-to-real gap. Moreover, a new domain adaptation (DA) method is presented to further bring the distribution of the real-flight data closer to that of the simulation data. The experimental results demonstrate that the DDCNN+DA model can increase the accuracy from 52.9% to 99.1% in real-world UAV fault detection.

翻译：识别螺旋桨故障对于保持四旋翼无人机安全高效运行至关重要。仿真到现实（sim-to-real）的无人机故障诊断方法为检测螺旋桨故障提供了一种经济高效且安全的途径。然而，由于仿真与真实环境之间存在差异，使用仿真数据训练的分类器通常在真实飞行中表现不佳。本研究提出了一种新颖的基于差异的深度卷积神经网络（DDCNN）模型来解决上述问题。该模型利用深度卷积神经网络提取的差异特征来减小仿真到现实的差距。此外，本文还提出了一种新的域自适应（DA）方法，以进一步使真实飞行数据的分布逼近仿真数据的分布。实验结果表明，DDCNN+DA模型能将真实场景无人机故障检测的准确率从52.9%提升至99.1%。