知识相关性图形引导的多源交互领域适应网络用于旋转机械故障诊断.
Zhenghong Wu1, Hongkai Jiang1, Xin Wang1
1School of Civil Aviation, Northwestern Polytechnical University, 710072 Xi'an, China.
ISA transactions
|August 12, 2023
概括
这项研究引入了一个用于旋转机械故障诊断的新型网络,改善了在没有标记数据的情况下跨领域的知识传输. 开发的方法通过对多个来源的知识进行关联来提高识别准确性.
科学领域:
- 机器学习 机器学习
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 由于分布差异和扩展数据类别,多源域的适应具有挑战性.
- 将知识从标记的源域调整到未标记的目标域对于现实的应用至关重要.
- 现有的方法在调整多个异质数据源以进行故障诊断方面存在困难.
研究的目的:
- 开发一个新的网络,用于旋转机械故障诊断,使用多源域调整.
- 在域调整中解决分布差异和数据类别扩展的挑战.
- 改进知识的交互和传播,跨多个标记的源域和一个未标记的目标域.
主要方法:
- 引入了一个以知识相关性图形为指导的多源交互领域适应网络 (KCGMIDAN).
- 利用跨时代更新的综合特征表示 (CFR) 来促进知识交互.
- 构建了一个知识相关图 (KCG) 进行跨领域的知识传播,并使用深度图网络进行样本识别.
主要成果:
- KCGMIDAN有效地促进了各个领域之间的知识互动和传播.
- 设计损失改善了类内紧性和特征的类间隔.
- 在旋转机械故障诊断中,与现有的领域适应方法相比,实现了更高的识别性能.
结论:
- 拟议的KCGMIDAN框架为旋转机械故障诊断提供了一个强大的解决方案.
- 知识相关图有效地弥合了不同数据领域之间的知识差距.
- 这种方法表明,对于需要无监督域调整的真实应用程序,这种方法具有显著的潜力.
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