Kang Shouqiang, Tang Xi, Wang Yujing, Wang Qingyan, Xie Jinbao
School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, Heilongjiang Province, China.
School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, Heilongjiang Province, China.
ISA Trans. 2024 Mar;146:195-207. doi: 10.1016/j.isatra.2023.12.019. Epub 2023 Dec 14.
To address the unknown spatial relationship between source and target domain labels, which leads to poor fault diagnosis accuracy, a contrastive universal domain adaptation model and rolling bearing fault diagnosis approach are proposed. The approach introduces bootstrap your own latent network to mine the data-specific structure of the target domain and proposes rejecting unknown class samples using an entropy separation strategy. Simultaneously, a source class weighting mechanism is designed to improve the transferable semantics augmentation method by assigning various class-level weights to source categories, which improves the alignment of the feature distributions in the shared label space to further construct fault diagnosis models. Experimental validation on two rolling bearing datasets confirmed the superior fault diagnosis accuracy of the proposed method under diverse working conditions.
为了解决源域和目标域标签之间未知的空间关系导致故障诊断准确率低下的问题，提出了一种对比通用域自适应模型和滚动轴承故障诊断方法。该方法引入自训练潜在网络来挖掘目标域的数据特定结构，并提出使用熵分离策略拒绝未知类样本。同时，设计了一种源类加权机制，通过为源类别分配不同的类级权重来改进可迁移语义增强方法，这改善了共享标签空间中特征分布的对齐，以进一步构建故障诊断模型。在两个滚动轴承数据集上的实验验证证实了该方法在不同工作条件下具有卓越的故障诊断准确率。
