使用压电传感器和机器学习的八旋翼结构分析
Andrzej Koszewnik1, Bartłomiej Ambrożkiewicz2, Daniel Ołdziej3
1Bialystok University of Technology, Wiejska Street 45C, Bialystok, 15-351, Poland. a.koszewnik@pb.edu.pl.
Scientific reports
|August 28, 2025
概括
这项研究引入了一种使用压电传感器和机器学习检测无人机驱动系统损坏的新方法. 它准确地识别电机故障,并跟踪无人机的损坏传播.
科学领域:
- 工程
- 机器人技术
- 结构健康监测
背景情况:
- 现有的无人机诊断通常集中在故障定位上.
- 需要了解局部电机故障如何在无人机中结构传播.
研究的目的:
- 开发和验证一种用于评估无人机驱动系统损伤及其空间传播的新诊断方法.
- 研究压电传感器和机器学习对无人机健康监测的有效性.
主要方法:
- 通过在单个无人机臂上改变PWM工作周期来模拟发动机损伤 (20%至80%).
- 每个手臂上的压电传感器记录了电压信号.
- 从传感器数据中提取时间和频率域的统计特征.
- 使用机器学习模型 (随机森林,KNN) 进行损害分类.
主要成果:
- 在受损的动力臂中获得高分类准确度 (93%-94%).
- 发现相反臂的精度较低 (50%-57%),表明损伤传播效应.
- 发现频域特征提高了诊断准确度,
结论:
- 拟议的方法有效地检测驱动系统损坏及其在无人机中的传播.
- 频域分析提高了分布式损伤的诊断能力.
- 这项研究有助于对无人机的健康监测和结构可靠性评估.
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