通过在潜伏子空间中的结构化预测,加强无监督轴承故障诊断
Chen Liu1, Runshan Hu2,3, Xuan Fang4
1School of Internet of Things Engineering, Wuxi Taihu University, Wuxi, 214064, China.
Scientific reports
|November 26, 2025
概括
本研究介绍了工业故障诊断的无监督领域适应框架,有效解决数据稀缺性和阶级不平衡问题. 这种新的方法显著提高了诊断的准确性,在现实世界的场景中超过了现有的方法.
科学领域:
- 工业工程 工业工程 工业工程
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 数据驱动的故障诊断对于工业设备的维护和运营效率至关重要.
- 现实世界的工业数据集经常遭受标签稀缺和严重的阶级失衡,阻碍了模型性能.
- 由于合成数据增强,现有的方法在域差异和有限的概括性方面扎.
研究的目的:
- 为工业故障诊断提出一个强大的无监督领域适应框架.
- 为了应对机械数据集中标签稀缺和阶级不平衡的挑战.
- 在现实应用中增强模型通用性和诊断准确性.
主要方法:
- 一个新的无监督域适应框架,通过插值合成故障信号.
- 整合条件域对抗网络 (CDAN) 进行域不变特征提取.
- 使用结构化伪标签和局部保护投影 (LPP) 进行域调整和阶级歧视.
主要成果:
- 拟议的方法在不平衡条件下达到91.10%的准确性,在带有基准数据集的CWRU上在平衡场景中达到84.65%的准确性.
- 在相应的条件下,性能超过了最先进的方法12.87%和3.57%.
- 废弃性研究证实了结构化伪标签对整体性能的重大贡献.
结论:
- 开发的框架证明了无监督工业故障诊断的有效性和稳定性.
- 这种方法有效地减轻了域差异,并提高了诊断准确度.
- 这种方法为现实世界的工业应用提供了一个有前途的解决方案,具有有限的标记数据.
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