一种两步传感器融合方法,通过音频和雷达测量来评估无人机螺旋的损坏
Gianluca Ciattaglia1, Giacomo Peruzzi2, Matteo Bertocco2
1Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了一种两步方法,用于检测和分类无人机 (UAV) 螺旋损坏. 机载机器学习模型使用声辐射识别损坏,确保无人机操作的关键安全性.
科学领域:
- 航空航天工程 航空航天工程
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 的使用越来越多,需要提高运营安全.
- 检测和分类部件损坏对于及时干预和事故预防至关重要.
- 螺旋损坏对无人机稳定性和飞行完整性构成重大风险.
研究的目的:
- 提出一种新的两步方法来检测和分类无人机螺旋损坏.
- 开发一个实时的车载系统,用于初步损坏评估.
- 整合一个二级系统,以准确确定损坏严重程度和位置.
主要方法:
- 车载,实时损坏检测使用声辐射和边缘处理与机器学习 (ML) 分类器.
- 一个基于地面站的二级雷达系统,用于无接触式振动和频率测量.
- 将声学和基于雷达的方法结合起来,形成一个全面的诊断系统.
主要成果:
- 嵌入式基于音频的ML模型在损坏分类中实现了99%以上的性能.
- 基于雷达的系统准确地区分和测量螺旋损坏的位置.
- 集成系统提供了对螺旋故障的时间响应和准确诊断.
结论:
- 拟议的两步方法有效地检测和分类无人机螺旋损坏.
- 该系统通过实现及时和适当的对策来提高无人机安全.
- 这种方法显著提高了无人驾驶飞行器操作的可靠性和安全性.
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