多源域自适应网络基于局部内核化的高阶时刻匹配,用于旋转机械故障诊断
Ying Zhang1, Jingjing Fan1, Zong Meng1
1Yanshan University, Qinhuangdao, PR China.
ISA transactions
|May 10, 2024
概括
本研究介绍了一种新的多源域自适应网络,用于旋转机械故障诊断. 该方法通过匹配域不变特征和融合分类器输出来实现超过99%的准确性.
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:
- 无监督的领域适应对于旋转机械跨领域故障诊断至关重要.
- 现有方法面临的挑战是,在不同运营领域的细粒度特征匹配方面存在挑战.
研究的目的:
- 开发一个多源域自适应网络,用于准确的旋转机械故障诊断.
- 为了增强域不变特征提取和类别子空间匹配.
主要方法:
- 一个多分支网络架构被设计用于源-目标域映射.
- 域对抗式学习被用来提取域不变特征.
- 局部内核化的高阶时刻匹配被用于细粒度子空间对齐.
- 基于本地域分布偏差的特征融合策略被实施.
主要成果:
- 在两个公共数据集上的实验验证证明了高性能.
- 双分支和三分支网络实现了超过99%的平均诊断准确率.
- 提出的方法被证明是有效的,并且优于现有的方法.
结论:
- 开发的多源域自适应网络有效地解决跨域故障诊断挑战.
- 当地的核心化高阶时刻匹配和特征融合策略提高了诊断准确度.
- 这种方法对现实世界的旋转机械健康监测具有重大前景.
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