This paper describes the development of an on-board data-driven system that can monitor and localize the fault in a quadrotor unmanned aerial vehicle (UAV) and at the same time, evaluate the degree of damage of the fault under real scenarios. To achieve offline training data generation, a hybrid approach is proposed for the development of a virtual data-generative model using a combination of data-driven models as well as well-established dynamic models that describe the kinematics of the UAV. To effectively represent the drop in performance of a faulty propeller, a variation of the deep neural network, a LSTM network is proposed. With the RPM of the propeller as input and based on the fault condition of the propeller, the proposed propeller model estimates the resultant torque and thrust. Then, flight datasets of the UAV under various fault scenarios are generated via simulation using the developed data-generative model. Lastly, a fault classifier using a CNN model is proposed to identify as well as evaluate the degree of damage to the damaged propeller. The scope of this paper focuses on the identification of faulty propellers and classification of the fault level for quadrotor UAVs using RPM as well as flight data. Doing so allows for early minor fault detection to prevent serious faults from occurring if the fault is left unrepaired. To further validate the workability of this approach outside of simulation, a real-flight test is conducted indoors. The real flight data is collected and a simulation to real sim-real test is conducted. Due to the imperfections in the build of our experimental UAV, a slight calibration approach to our simulation model is further proposed and the experimental results obtained show that our trained model can identify the location of propeller fault as well as the degree/type of damage. Currently, the diagnosis accuracy on the testing set is over 80%.

翻译：本文描述了开发一种机载数据驱动系统，用于监测和定位四旋翼无人机（UAV）的故障，同时在实际场景下评估故障的损伤程度。为实现离线训练数据生成，提出了一种混合方法，通过结合数据驱动模型与描述无人机运动学的成熟动力学模型，开发虚拟数据生成模型。为有效表征故障螺旋桨的性能下降，提出了深度神经网络的一种变体——LSTM网络。该螺旋桨模型以螺旋桨转速（RPM）为输入，基于螺旋桨故障状态估计产生的扭矩和推力。随后，利用所开发的数据生成模型通过仿真生成无人机在各种故障场景下的飞行数据集。最后，提出基于CNN模型的故障分类器，用于识别并评估受损螺旋桨的损伤程度。本文的研究范围聚焦于使用RPM及飞行数据对四旋翼无人机进行故障螺旋桨识别与故障等级分类。这有助于早期检测微小故障，防止因故障未修复而引发严重问题。为进一步验证该方法在仿真之外的可行性，在室内进行了真实飞行测试。收集真实飞行数据并开展了仿真到真实的"Sim-to-Real"测试。针对实验无人机装配存在的缺陷，进一步提出了对仿真模型的轻微校准方法，实验结果表明，训练后的模型能够识别螺旋桨故障位置及损伤程度/类型。当前测试集上的诊断准确率超过80%。