[论文解读] Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Model

Jianheng Tong, Wěi Zhāng+3 more|arXiv (Cornell University)|Feb 3, 2023

Advanced Measurement and Detection Methods被引用 6

一句话总结

该论文提出了一种混合数据生成模型，结合基于物理的无人机动力学与数据驱动的LSTM网络，以模拟四旋翼无人机中螺旋桨故障行为，从而训练基于CNN的故障分类器，利用真实飞行数据验证，该分类器在识别螺旋桨故障位置和严重程度方面准确率超过80%，并通过端到端仿真到真实世界的迁移实现了验证。

ABSTRACT

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%.

研究动机与目标

• 开发一种机载、数据驱动的系统，用于实时监测和定位四旋翼无人机的螺旋桨故障。
• 通过混合仿真模型生成合成故障场景，解决无人机中真实故障数据稀缺的问题。
• 实现对轻微螺旋桨故障的早期检测，以防止灾难性故障的发生。
• 通过仿真到真实世界的迁移框架，利用真实飞行数据验证故障检测系统。
• 利用飞行数据和转速（RPM）数据对故障严重程度等级（如轻微、中等、严重）进行分类。

提出的方法

• 一种混合数据生成模型将基于物理的无人机运动学模型与数据驱动的LSTM网络相结合，以模拟螺旋桨故障动力学。
• LSTM模型基于输入的RPM和故障状态预测推力和扭矩输出，模拟性能退化。
• 通过混合模型的仿真生成不同故障场景下的飞行数据集。
• 基于模拟数据训练基于CNN的故障分类器，以检测和分类故障类型及严重程度等级。
• 使用真实室内飞行数据进行仿真到真实世界的迁移测试，并应用模型校准以补偿硬件缺陷。
• 系统以RPM和飞行状态数据作为输入，用于故障检测与严重程度评估。

实验结果

研究问题

• RQ1 混合数据生成模型能否有效模拟四旋翼无人机中真实的螺旋桨故障场景？
• RQ2 基于LSTM的模型能否在不同故障条件下准确预测推力和扭矩的退化？
• RQ3 在模拟数据上训练的CNN分类器能否泛化到真实飞行数据中实现故障检测？
• RQ4 该系统在真实飞行场景中对故障严重程度等级（如轻微、中等、严重）的分类能力如何？
• RQ5 仿真到真实世界的迁移方法在存在硬件缺陷的情况下，其检测准确率的保持效果如何？

主要发现

• 混合数据生成模型通过结合物理无人机模型与学习到的LSTM行为，成功模拟了真实的螺旋桨故障动力学。
• LSTM模型有效捕捉了在故障状态下RPM与输出推力/扭矩之间的关系。
• 基于CNN的故障分类器在使用模拟数据的测试集上实现了超过80%的诊断准确率。
• 真实飞行验证表明，训练后的模型能够正确识别螺旋桨故障的位置和类型。
• 对仿真模型进行校准步骤显著提升了从仿真到真实飞行数据迁移时的性能。
• 该系统可实现对轻微故障的早期检测，支持预防性维护并提升无人机安全性。