基于超图神经网络的轴承的合故障诊断
Shenglong Wang1, Xiaoxuan Jiao1, Bo Jing1
1Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究介绍了两种超图神经网络框架,用于诊断机械设备中的复杂合故障. 提出的方法实现了高诊断准确度,为识别多个同时发生的设备故障提供了有效的解决方案.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 合故障,涉及多个基本故障类型和高阶关系,在机械设备中很常见.
- 现有的诊断方法很难有效地模拟这些复杂的多缺陷相互作用.
研究的目的:
- 提出新的超图神经网络 (HNN) 架构,用于诊断高阶合故障.
- 评估两个不同的基于HNN的故障诊断框架的有效性.
主要方法:
- 开发了两个HNN框架:一个用于特征生成 (使用HNN生成负样本),另一个用于特征提取 (使用多头注意力和HNN聚合).
- 在特征生成框架中,基础故障是节点,超边缘连接它们;HNN生成合故障.
- 在特征提取框架中,节点代表故障模式,超边缘链接常见故障模式,内部产品相关性诊断故障.
主要成果:
- 功能生成框架在合故障方面实现了88.6%的诊断准确率.
- 功能提取框架在合故障方面达到86.76%的诊断准确率.
- 两种拟议的框架都在诊断和分析高阶合故障方面表现出强的表现.
结论:
- 超图神经网络为建模和诊断复杂的合故障提供了强大的方法.
- 两种拟议的HNN架构为机械设备故障诊断提供了有效的解决方案,实现了高精度.
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