用飞行数据检测多旋翼螺旋桨损伤类型与程度
Propeller damage detection, classification and estimation in multirotor vehicles
- 仅用惯性数据和控制指令训练复合模型
- 能准确识别三种损伤类型并估计严重程度
- 适用于各类多旋翼平台，无需额外传感器
本文提出一种数据驱动框架，用于检测、识别和量化多旋翼无人机螺旋桨的损伤。通过替换一个螺旋桨为不同损伤程度的受损叶片，采集了真实飞行数据。利用这些数据训练了一个包含分类器和神经网络的复合模型，能够准确识别故障类型、估计损伤严重程度，并定位受损旋翼。所用数据仅来自惯性测量和控制命令输入，确保该方法可适配多种多旋翼平台。
原文摘要 · Abstract (English)
This manuscript details an architecture and training methodology for a data-driven framework aimed at detecting, identifying, and quantifying damage in the propeller blades of multirotor Unmanned Aerial Vehicles. By substituting one propeller with a damaged counterpart-encompassing three distinct damage types of varying severity-real flight data was collected. This data was then used to train a composite model, comprising both classifiers and neural networks, capable of accurately identifying the type of failure, estimating damage severity, and pinpointing the affected rotor. The data employed for this analysis was exclusively sourced from inertial measurements and control command inputs, ensuring adaptability across diverse multirotor vehicle platforms.
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