根据联合机制的歧视性故障诊断转移学习网络
Yuxuan Yang1, Jiarui Jing1, Jian Zhang1
1College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China.
Scientific reports
|March 15, 2025
概括
本研究引入了一种新的无监督转移学习方法,用于诊断旋转机械故障. 该方法增强了特征提取和域调整,大大提高了故障分类准确性.
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:
- 对于旋转机械而言,无监督故障诊断至关重要,但因特征提取和域移动而受到挑战.
- 现有的方法在有效的特征学习和在不同操作领域对数据分布进行对齐方面扎.
研究的目的:
- 提出一种新的无监督转移学习方法,用于旋转机械故障诊断.
- 为了增强特征提取,改善域分布对齐,并减少域转移以实现准确的故障分类.
主要方法:
- 整合Squeeze-and-Excitation (SE) 的注意力机制,以增强特征.
- 开发一个综合分布对齐框架 (IDAF) 使用联合适应网络 (JAN) 与本地最大平均差异和关联对齐 (CORAL).
- 应用I-Softmax损失用于歧视性特征学习和深度对抗性训练,用于域调整.
主要成果:
- 拟议的方法有效地增强了相关特征,并抑制了多余的特征.
- 在源域和目标域之间实现了更好的分布对齐.
- 在使用轴承和轮故障数据集的无监督故障诊断任务中表现出卓越的性能.
结论:
- 新的无监督转移学习方法显著提高了旋转机械的故障分类准确性.
- 集成SE注意力,IDAF,I-Softmax损失和对抗性培训,为领域转移挑战提供了强大的解决方案.
- 该方法在工业机械健康监测方面具有很强的实际应用潜力.
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