CNN和GCN的组合模型用于机器故障诊断
Qianqian Zhang1, Caiyun Hao1, Zhongwei Lv1
1School of Automation and Software Engineering, Shanxi University, Taiyuan, P.R. China.
PloS one
|October 5, 2023
概括
这项研究引入了一种新的图形注意力卷积神经网络 (GACNN) 用于机器故障诊断. GACNN有效地提取多层特征,提高诊断准确度,特别是在杂的环境中.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 机器故障诊断依赖于歧视性特征学习.
- 卷积神经网络 (CNN) 捕获全球特征,但错过了信号关系.
- 图形卷积网络 (GCNs) 擅长挖掘具有拓结构的数据中的关系.
研究的目的:
- 为增强机器故障诊断提出混合图表注意力卷积神经网络 (GACNN).
- 为了利用CNN和GCN的优势,进行全面的特征提取.
- 改进特征表示和诊断性能,特别是在噪音条件下.
主要方法:
- 开发了一个GACNN框架,完全集成CNN和GCN子网络.
- 采用高效通道注意力 (ECA) 机制来最大限度地减少信息丢失.
- 在三个不同的数据集上验证了方法.
主要成果:
- GACNN框架显示了功能表示能力的改进.
- 与现有的故障诊断方法相比,实现了竞争力的准确性.
- 即使在具有大量背景噪音的环境中,也表现出卓越的性能.
结论:
- 拟议的GACNN是有效的智能故障诊断.
- 混合方法通过结合全球和关系信息来增强特征提取.
- 在具有挑战性的条件下,GACNN为机器故障诊断提供了强大的性能.
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