为旋转机器故障诊断提供无源域调整框架
Hoejun Jeong1, Seungha Kim1, Donghyun Seo1
1Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究引入了一个强大的故障诊断框架,用于适应新环境的旋转机械. 拟议的方法显著提高了域移动中的性能,提高了智能故障检测的可靠性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 旋转机械的智能故障诊断面临由于领域转移而导致性能恶化.
- 当前的方法在应用于新的,未见的操作环境时,往往缺乏稳定性.
研究的目的:
- 开发一个强大的故障诊断框架,有效地解决旋转机械的领域转移.
- 提高故障诊断系统在实际,现实世界的场景中的适应性和性能.
主要方法:
- 实施了基于订单频率的预处理方法,以使旋转变化正常化.
- 使用U-Net变异自编码器 (U-NetVAE) 通过重建学习进行适应.
- 采用了测试时间培训 (TTT) 策略,用于无监督的目标域调整.
主要成果:
- 拟议的框架在F1分数和跨域回忆方面显著超过了传统的机器学习和深度学习模型.
- 在具有挑战性的条件下,在目标域中实现了0.47的F1得分和0.51的回忆.
- 废弃性研究验证了每个成分在改善适应性能的有效性.
结论:
- 开发的框架显示出在领域转移下智能故障诊断的卓越稳定性和适应性.
- 结合机械先验,自主监督学习和轻量化适应策略,对于实际的故障诊断是有效的.
- 该方法为在各种操作环境中可靠检测故障提供了一个有希望的解决方案.
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