一个基于多源传感器的两阶段重要感知子图卷积网络,用于跨域故障诊断
Yue Yu1, Youqian He1, Hamid Reza Karimi1
1Department of Mechanical Engineering, Politecnico di Milano, via La Masa 1, Milan 20156, Italy.
概括
这项研究介绍了一种新的两阶段图形卷积网络 (GCN),用于改进预测和健康管理. 重要感知子图 GCN (I^2SGCN) 增强了使用多源传感器和精细特征提取的跨域故障诊断.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 预测和健康管理的预测.
背景情况:
- 图形卷积网络 (GCNs) 通过提取节点特征和关系,在预测和健康管理方面表现出色.
- 现有的GCN方法在图形质量,可变条件和有限的数据方面扎,阻碍了性能.
- 从单个传感器中很难获得高质量的图形,这会影响诊断的准确性.
研究的目的:
- 为了解决预测和健康管理的GCN的局限性.
- 为增强跨领域故障诊断提出一个两阶段的重要意识子图 GCN (I^2SGCN).
- 在可变的工作条件和有限的数据下改善诊断性能.
主要方法:
- 利用多源传感器构建更丰富的故障信息图表.
- 在故障诊断中实施了无监督域调整 (UDA) 的两阶段框架.
- 在第一阶段利用多实例学习来估计传感器重要性因素.
- 在第二阶段开发了I^2SGCN,用于精细的跨领域故障诊断.
- 构建基于特征和基于重要性的图表,用于联合关系挖矿.
- 引入了一个子图学习策略,以丰富功能和规范化培训.
主要成果:
- 拟议的I^2SGCN方法在跨领域故障诊断中表现出卓越的性能.
- 在四个案例研究中的实验验验证了两阶段方法的有效性.
- 该方法在诊断准确性方面超过了现有的最先进的技术.
- 亚图学习策略增强了特征表示和模型概括.
结论:
- I^2SGCN框架有效地克服了预测和健康管理领域现有的GCN的局限性.
- 具有重要意识的子图学习的双阶段UDA方法显著改善了跨领域的故障诊断.
- 多源传感器数据和精细的特征提取对于强大的机械设备健康监测至关重要.
相关概念视频
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