AttenResNet18:用于滚动轴承的新型跨领域故障诊断模型
Gangjin Huang1,2, Shanshan Wu3, Yingxiao Zhang3
1College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan 618307, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究引入了注意力增强的残余网络 (AttenResNet18),用于在滚动轴承中进行强大的跨领域故障诊断. 这种新的方法通过动态调整特征分布并减少噪声脆弱性,显著提高了准确性.
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:
- 滚动轴承的跨领域故障诊断面临由于领域转移的挑战.
- 现有的域适应方法往往容易受到噪音的影响,并且无法充分解决边际和条件分布差异.
研究的目的:
- 开发一种创新的故障诊断技术,用于滚动轴承的跨领域应用.
- 增强特征表示,减少故障诊断中的噪声脆弱性.
主要方法:
- 使用了注意力增强的剩余网络 (AttenResNet18),用于特征提取,具有一维注意力机制.
- 提出了一个动态平衡分布适应 (DBDA) 机制,结合了MMD-CORAL融合度量 (MCFM).
- 使用适应因子来动态平衡边际和条件分布适应.
主要成果:
- 在两个滚动轴承数据集上,AttenResNet18实现了99.89%的平均准确性.
- 拟议的方法证明了增强的特征表示和减少对噪声的脆弱性.
- 与现有方法相比,在故障检测精度上观察到显著的改进.
结论:
- 带有DBDA机制的AttenResNet18提供了一个强大而准确的解决方案,用于滚动轴承的跨域故障诊断.
- 该方法有效地处理域移动和噪声,改善新任务的诊断性能.
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