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一种基于双流混合域适应的轴承故障诊断方法

Xinze Jiao¹, Jianjie Zhang¹, Jianhui Cao¹

¹College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China.

Sensors (Basel, Switzerland)

|June 27, 2025

概括

这项研究引入了一种用于轴承故障诊断的新型网络,在不断变化的条件下提高了准确性. 该方法通过结合时间和频率域特征,有效处理有限的标记数据.

科学领域:

机械工程机械工程
机器学习机器学习
信号处理信号处理

背景情况:

轴承故障诊断对于工业机械的维护至关重要.
挑战包括域名转移和在现实场景中标记数据的稀缺性.
现有的方法在不同的操作条件和有限的标记样本下扎.

研究的目的:

开发一种有效的轴承故障诊断方法,用于在有限的标记数据下,在不同的操作条件下进行诊断.
建议建立一个双流混合域适应网络 (DS-HDA Net),以进行可靠的故障识别.
提高轴承故障诊断系统的准确性和可靠性.

主要方法:

一个双流混合域适应网络 (DS-HDA Net) 融合了CNN提取的时间域和MLP处理的频域特征.
层次域适应包括边际分布适应 (MDA) 和条件域适应 (CDA).
一种软伪标签生成机制,使用高斯混合模型 (GMM) 和Mahalanobis距离来获得未标签的数据.

主要成果:

DS-HDA Net实现了高平均准确率:在帕德博恩大学数据集上达到99.43%,在江南大学数据集上达到99.56%.
提出的方法显著优于现有的最先进的技术.
即使在标记数据要求最低的情况下,也证明了有效的故障诊断.

结论:

在复杂的操作条件下,DS-HDA Net有效地解决了轴承故障诊断的挑战.
混合域适应和软伪标签方法提高了诊断性能.
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,需要在有限的标记数据下准确检测故障.

关键词:

轴承轴承是指轴承的轴承.域名适应域名适应双流网络是双流网络.错误诊断错误诊断错误诊断是一个问题.伪标签是一种伪标签.转移学习转移学习

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