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自我注意力增强的GraphSAGE用于使用振动信号进行无人机故障诊断

Yumeng Ma¹, Yuhan Sun¹, Ligang Chen²

¹School of Aeronautical Engineering, Shandong University of Aeronautics, Binzhou, 256600, China.

Scientific reports

|December 12, 2025

概括

本研究引入了一种先进的GraphSAGE-SA模型,用于使用振动数据诊断无人机故障. 该方法显著提高了故障分类的准确性,提高了无人机的安全性和可靠性.

科学领域:

机械工程机械工程
人工智能的人工智能
航空航天工程航空航天工程

背景情况:

无人驾驶飞行器 (UAV) 的传统故障诊断方法在从复杂的振动信号中提取关键特征方面面临挑战.
有效的故障诊断对于确保无人机操作安全,任务可靠性和防止灾难性故障至关重要.

研究的目的:

提出一个增强的故障诊断框架,GraphSAGE-SA,有效地从无人机振动数据中提取故障特征.
提高无人机和类似复杂机械系统的智能故障诊断系统的准确性和稳定性.

主要方法:

使用MPU6050传感器开发了一个定制的振动数据采集系统.
利用K-最近邻近 (KNN) 算法将1D振动信号转换为图形结构数据.
实现了一个GraphSAGE-SA模型,具有对层次信息聚合和自适应邻居重要性加权的自我注意机制.

主要成果:

在四旋翼无人机平台上达到98%的故障分类准确度.
与传统的GraphSAGE变体 (3-12%的改进) 和其他基于图表的方法相比,表现出更高的性能.
GraphSAGE-SA模型有效地捕捉了振动数据中的本地结构模式和全球依赖关系.

结论:

拟议的GraphSAGE-SA框架为无人机智能故障诊断提供了一种新且有效的解决方案.
自主注意机制提高了特征提取精度,从而大大提高了诊断准确度.
这种方法有望提高无人机操作和其他复杂机械的安全性和可靠性.

关键词:

图表神经网络的神经网络图形SAGE 图形SAGE 图形SAGE 图形SAGE 图形SAGE 图形SAGE 自我注意力机制机制无人机故障诊断无人机故障诊断对振动信号进行分析.

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