一个自我注意力Legendre图形卷积网络用于旋转机械故障诊断
Jiancheng Ma1, Jinying Huang1,2, Siyuan Liu1
1School of Computer Science and Technology, North University of China, Taiyuan 030051, China.
这项研究引入了一种新的莱根德尔图形卷积网络 (LGCN),用于旋转机械故障诊断. 自主注意力图汇集方法提高了检测变速箱故障的准确性和适应性.
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:
- 旋转机械的健康对于工业运作至关重要.
- 传统的深度学习方法在故障诊断中错过了关系信息.
- 准确的故障诊断确保了安全和效率.
研究的目的:
- 开发一个先进的深度学习模型,用于旋转机械故障诊断.
- 克服特征提取现有方法的局限性.
- 为了提高故障检测的稳定性和效率.
主要方法:
- 提出了一个Legendre图形卷积网络 (LGCN),与自我注意图形聚合 (SA-LGCN) 集成.
- 从欧几里德空间转换到非欧几里德空间的振动信号 (图形信号).
- 利用基于莱根德尔多项式的快速局部光谱过器.
主要成果:
- 该SA-LGCN模型在故障诊断准确度方面显示出显著的优势.
- 该方法显示在各种工作条件下提高了负载适应性.
- 在10个不同的行星变速箱故障任务中实现了卓越的性能.
结论:
- SA-LGCN模型为旋转机械故障诊断提供了一个强大的解决方案.
- 该方法有效地捕获振动信号中的关系信息.
- 这种方法提高了工业设备的诊断能力.
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