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.

翻译：监测螺旋桨故障对维护四旋翼无人机安全可靠运行至关重要。基于仿真到现实迁移的无人机故障诊断技术为识别螺旋桨故障提供了安全经济的方案。然而，受户外场景中风扰影响，使用模拟数据训练的分类器在实际飞行中表现不佳。本文提出了一种基于不确定性的故障分类器（UFC），以应对风环境下无人机故障诊断面临的仿真到现实迁移挑战。该分类器采用基于差异的深度卷积神经网络集成（EDDCNN）降低模型方差与偏差，并利用基于不确定性的决策框架过滤不可靠预测。实验结果表明，在户外风场景下，UFC能以33.6%的数据使用率实现100%的故障诊断准确率。