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.

翻译：螺旋桨故障的准确诊断对于确保四旋翼飞行器安全高效运行至关重要。利用仿真数据训练故障分类器并将其部署到真实四旋翼飞行器上，是一种经济且安全的方法。然而，仿真到现实的差距常导致分类器在实际飞行中性能较差。本文提出一种深度学习模型，通过采用新识别特征作为输入并利用领域自适应技术缩小仿真到现实的差距，从而解决该问题。此外，我们引入一种调整后的仿真模型，以生成更准确反映真实四旋翼飞行器行为的训练数据。实验结果表明，所提方法在检测螺旋桨故障时准确率达到96%。据我们所知，这是首个可靠且高效的四旋翼螺旋桨仿真到现实故障诊断方法。