可解释的动态图学习和多尺度特征融合用于液压系统健康监测
Ziheng Gu1, Xiansong He1, Yibo Song1
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
一个新的动态多尺度图形神经网络 (DMS-GNN) 增强了液压系统故障诊断. 这种先进的方法通过动态学习传感器关系实现了98.47%的准确性,改善了安全关键系统监控.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 液压系统在航空航天和工业中至关重要,需要可靠的健康监测.
- 传统方法在动态传感器相关性和多分辨率特征提取方面扎,尤其是在有限的数据的情况下.
研究的目的:
- 提出一个新的动态多尺度图神经网络 (DMS-GNN),用于准确的液压系统故障诊断.
- 解决静态图形结构和基于网格的模型在捕获不断变化的传感器数据方面的局限性.
主要方法:
- 开发了一个分层的多级特征提取模块,用于各种故障签名.
- 引入了一个基于自我注意的动态图学习器,以自适应地推断传感器拓.
- 在电液测试台上验证了方法.
主要成果:
- 拟议的DMS-GNN实现了98.47%的诊断准确度.
- 超越了包括GraphSAGE,静态GCN和GAT在内的最先进的方法.
- 证明了强大的多传感器融合诊断能力.
结论:
- DMS-GNN有效地将多尺度时间学习与动态空间推理相结合.
- 该方法在液压系统健康监测方面取得了重大进展.
- 动态图表学习对于在故障诊断中演变的传感器相关性至关重要.
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