一个空间时间联合诊断框架通过图形卷积和注意力增强的双向门网来处理轴承故障
Zhiguo Xiao1,2,3, Xinyao Cao2, Huihui Hao3
1School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100811, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种用于滚动轴承故障诊断的新型联合诊断框架,集成图形卷积网络 (GCN) 和注意力增强的双向封闭反复单元 (BiGRU),以提高准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承故障诊断对于工业机械维护至关重要.
- 现有的方法难以充分建模时空合和多模式相关性.
研究的目的:
- 为滚动轴承故障诊断提出一个先进的联合诊断框架.
- 增强时空特征和多模式关联的建模.
主要方法:
- 利用图形卷积网络 (GCNs) 来进行空间特征提取.
- 实施了注意力增强的双向封闭反复单位 (BiGRUs) 用于时间建模.
- 开发了一个联合学习架构,将GCN和BiGRU与时空图集成在一起.
主要成果:
- 在公共数据集 (例如,CWRU) 上实现了97.08%的分类准确性.
- 通过动态空间拓模型证明了轴承信号的有效解.
- 成功地结合了多尺度的时空特征,以准确地描述断路冲击.
结论:
- 拟议的GCN-BiGRU框架显著提高了滚动轴承故障诊断性能.
- 该方法提供了一种强大的方法,用于深度融合时空特征.
- 这个框架准确地捕捉了轴承故障影响特征,提高了诊断能力.
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