一种用于滚动轴承的故障诊断方法,在开放的领域适应与对抗性学习
Tongfei Lei1, Feng Pan2, Jiabei Hu3
1School of Mechanical Engineering, Xijing University, Xi'an, 710123, China. leitongfei@xijing.edu.cn.
Scientific reports
|March 29, 2025
概括
本研究引入了对抗性学习领域适应方法,用于开放式故障诊断. 该方法有效地识别未知的故障,并调整已知的类别,在工业环境中实现高诊断精度.
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:
- 工业故障诊断通常假定是一个封闭的环境,这是不切实际的,因为不同的操作条件和数据分布转移.
- 封闭集假设的失败需要强大的方法来进行开放集故障诊断,能够处理未知的故障类型.
研究的目的:
- 开发一种使用对抗式学习的领域适应方法,用于工业应用中的开放式故障诊断.
- 在不同的工作条件下有效区分已知和未知的故障类别.
主要方法:
- 为了有效地提取故障特征,采用了卷积自编码器.
- 通过加权类相似性来建立一个未知的边界,以对齐跨域的共享类.
- 使用对抗性学习来确保域调整和准确分类已知的故障,同时识别未知样本.
主要成果:
- 拟议的方法在三个滚动轴承数据集中实现了96.60%,96.56%和96.62%的高平均诊断F1分数.
- 该技术证明了在目标域中有效拒绝未知的故障数据.
- 已知类的成功对齐得到了实现,证实了该方法在开放世界假设下的能力.
结论:
- 开发的域适应方法与对抗性学习对于开放式故障诊断非常有效.
- 该方法成功地应对了工业环境中各种工作条件和未知的故障数据的挑战.
- 该方法在开放世界假设下验证了其故障诊断能力,优于传统的封闭式方法.
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