对无人驾驶飞行器发动机故障诊断的皮埃佐传感器放置的非线性复发性分析
Andrzej Koszewnik1, Leszek Ambroziak2, Daniel Ołdziej2
1Bialystok University of Technology, Wiejska Street 45C, Bialystok, 15-351, Poland. a.koszewnik@pb.edu.pl.
Scientific reports
|April 9, 2024
概括
这项研究使用压缩传感器和非线性复发分析来诊断多直升机无人机 (UAV) 推进系统. 最佳的传感器放置在手臂侧面,可以提高故障检测和运动健康监测.
科学领域:
- 工程 工程师 工程师 工程师
- 航空航天工程 航空航天工程
- 振动分析 振动分析
背景情况:
- 多飞机无人机 (UAV) 需要强大的推进系统诊断.
- 在故障操作期间的过渡状态 (例如,电机阶段问题) 需要有效的监控.
- 使用压电传感器进行振动分析提供了一个有前途的诊断方法.
研究的目的:
- 调查压电传感器的最佳放置,以有效诊断多飞机推进系统.
- 用非线性方法分析振动时间序列,用于故障检测.
- 为了评估不同传感器位置对推进系统变化的灵敏度.
主要方法:
- 对振动时间序列数据进行非线性分析.
- 在短时间窗口中应用的反复性量化分析.
- 主要组件分析 (PCA) 用于分析复发量化.
- 测试不同脉冲宽度调制 (PWM) 工作周期和传感器位置 (侧面与顶部).
主要成果:
- 压电传感器对振动很敏感,这表明推进系统的健康状况.
- 非线性复发分析有效地检测到无人机电机速度的变化.
- 压力传感器的侧面位置对推进系统变化的敏感性更高.
- 主要组件分析证实了侧面传感器位置的优越性.
结论:
- 非线性复发分析是诊断多飞机推进系统的可行方法.
- 最佳的压力传感器放置,特别是在手臂的侧面,显著提高了诊断的有效性.
- 这项研究为加强多飞机电机的健康监测和故障检测提供了基础.
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