基于转移学习的设备故障诊断:域泛化方法
Weihong Wang1, Xinwen Zhao1, Yongfa Zhang1
1School of Nuclear Science and Technology, Naval University of Engineering, Wuhan, 430033, China.
Scientific reports
|November 21, 2025
概括
一种新的机器学习方法FOCAL-TFAM-ResNet-LSTM通过解决数据分布不一致性来改善故障诊断. 这种方法在复杂的工程场景中提高了准确性,超过了现有的技术.
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:
- 传统的数据驱动故障诊断需要相同的培训和测试数据分布,限制其在工程中的应用.
- 源域和目标域之间不一致的数据分布对准确的故障诊断构成了重大挑战.
研究的目的:
- 提出一种新的故障诊断方法,FOCAL-TFAM-ResNet-LSTM,能够处理不一致的数据分布.
- 在工程应用中提高基于机器学习的故障诊断的准确性和稳定性.
主要方法:
- 开发了一种基于映射的深度转移学习方法,结合了面向特征的相关性对齐层 (FOCAL) 来实现非线性分布对齐.
- 引入了一个具有梯度动态重量优化的自适应度函数,以平衡域调整和分类准确性.
- 在ResNet-LSTM网络中集成了一个时间频率注意模块 (TFAM),以减轻长期序列训练中的梯度消失.
主要成果:
- 在不利条件下,FOCAL-TFAM-ResNet-LSTM方法实现了96.83%的分类准确度,在反转数据集上损失0.1024 (SNR为-5dB).
- 与MMD和MK-MMD等传统域调整技术相比,显示出显著的改进,精度提高了8.42%,损失减少了0.3594.
- 通过互换和Case Western Reserve大学的数据集验证了算法的有效性.
结论:
- 拟议的FOCAL-TFAM-ResNet-LSTM方法有效地解决了故障诊断中数据分布不一致的挑战.
- 与现有方法相比,这种新的方法提供了更高的性能,特别是在复杂和杂的工程环境中.
- 该研究强调了深度转移学习和注意力机制对于强大的故障诊断系统的潜力.
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