用WPD增强的深度图形对比学习数据融合用于滚动轴承故障诊断
Ruozhu Liu1, Xingbing Wang2, Anil Kumar2
1School of International Education, Jiaxing Nanyang Polytechnic Institute, Jiaxing 314000, China.
Micromachines
|July 29, 2023
概括
这项研究引入了一种新的方法,用于使用振动信号来诊断滚动轴承故障. 该技术提高了故障检测的准确性和速度,确保机械系统的顺运行.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 滚动轴承是机械系统中的关键部件,早期故障检测对于运行效率和经济原因至关重要.
- 目前用于分析来自轴承的未标记时间序列振动数据的现有方法在全面提取故障信息方面存在局限性.
- 准确的故障诊断对于防止意外故障和降低维护成本至关重要.
研究的目的:
- 为滚动轴承提出一种新的故障诊断技术,利用1D振动信号的图形节点级故障信息.
- 加强对未标记的时间序列数据的分析,以便更全面地进行滚动轴承故障诊断.
- 开发一种提高机械系统故障诊断的准确性和速度的方法.
主要方法:
- 来自滚动轴承的振动信号被采样并使用波束包分解 (WPD) 来处理,以提取波束能量作为节点特征.
- 使用Pearson相关系数 (PCC) 构建了一个节点图,用于边缘重量,表示轴承健康状况.
- 图形卷积神经网络 (GCN) 和深度图形对比学习 (DGCL) 用于加密和分类增强节点图.
主要成果:
- 拟议的波形包分解-皮尔森相关系数-深度图对比学习 (WPDPCC-DGCL) 方法在与现有的基于对比学习的故障诊断技术相比,表现优越.
- 该方法有效地通过图形节点级故障样本和多尺度特征编码捕获底层故障信息.
- 实验结果证实了该方法能够快速准确地诊断滚动轴承的故障.
结论:
- WPDPCC-DGCL方法通过结合WPD和GCN功能,为滚动轴承故障诊断提供了一种灵活而强大的方法.
- 该技术有效地从振动信号中提取和分析故障信息,从而提高诊断准确度.
- 这种方法通过及时准确地检测故障,确保机械系统的正常运行.
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