端到端的多尺度残余网络,并行关注机制,用于在噪音和小样本下进行故障诊断
Yawei Sun1, Hongfeng Tao1, Vladimir Stojanovic2
1Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi, 214122, PR China.
ISA transactions
|December 22, 2024
概括
这项研究引入了一种新型的多尺度残余网络,具有用于稳固故障诊断的并行注意机制. 该模型在小样本数据集上实现了高精度,即使有显著的噪音干扰.
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:
- 错误诊断面临的挑战是噪音大,数据量小或数据量混合.
- 传统的神经网络在复杂的故障特征提取方面扎.
研究的目的:
- 开发一个端到端的多尺度残留网络,并行关注机制.
- 为了增强复杂故障诊断场景的特征提取能力.
主要方法:
- 适应性混合组合,以保留计时信号的特征.
- 平行注意力机制用于道和时间域特征分析.
- 多尺度特征平行融合用于捕捉各种特征尺度.
主要成果:
- 在小样本故障诊断数据集上实现了高精度 (99.67%-99.83%).
- 即使在0dB的噪音水平下,也保持了60% - 80%的准确性.
- 在复杂的故障诊断中表现出卓越的性能.
结论:
- 拟议的网络有效地解决了故障诊断数据集中的挑战.
- 平行注意力和多尺度融合显著改善了特征提取.
- 该模型为现实世界故障诊断应用提供了强大的解决方案.
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