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滚动轴承的跨领域故障诊断方法基于对比的通用领域适应

Shouqiang Kang¹, Xi Tang¹, Yujing Wang¹

¹School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, Heilongjiang Province, China.

ISA transactions

|December 28, 2023

概括

这项研究引入了一个新的域适应模型,用于改进滚动轴承故障诊断. 该方法通过调整数据分布和拒绝未知样本来提高准确性,这对于工业机械健康至关重要.

科学领域:

机械工程机械工程
机器学习机器学习
人工智能的人工智能

背景情况:

滚动轴承的故障诊断对于工业设备的可靠性至关重要.
现有的方法在源域和目标域之间的未知空间关系中扎,限制了准确性.
需要域调整技术来弥合不同操作条件之间的差距.

研究的目的:

为增强滚动轴承故障诊断提出一个对比的通用领域适应模型.
为了应对源域和目标域标签之间的未知空间关系的挑战.
在不同的工作条件下提高故障诊断模型的准确性和稳定性.

主要方法:

引入了一个引导你自己的隐藏网络来挖掘目标域数据结构.
实施了分离策略,以拒绝未知的类样本.
为可转移语义增强设计了一个源类权重机制.

主要成果:

拟议的方法在两个滚动轴承数据集上证明了卓越的故障诊断准确性.
在共享标签空间中实现了特征分布的有效对齐.
该模型在各种工作条件下显示出强度.

结论:

开发的对比的通用域适应方法显著改善了滚动轴承故障诊断.
隐藏网络挖矿,分离和类权重的整合提高了模型性能.
这种方法为现实世界的工业故障诊断应用提供了有前途的解决方案.

关键词:

相反的学习学习.错误诊断是一个错误的诊断.滚动轴承是一个滚动轴承.全球域名适应的适应.

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