Abstract: To address the challenges of label scarcity and fine-grained feature alignment in rolling bearing fault diagnosis under variable speed conditions, this paper proposes a Category-Aware Contrastive Learning (CACL) method driven by coupled time-frequency attention for unsupervised cross-domain diagnosis. First, for feature extraction, a coupled time-frequency attention module is constructed to simultaneously extract discriminative features from both time and frequency domains of fault signals while enhancing sensitivity to long-tail distributions and incipient faults. Second, the extracted deep discriminative features are fed into a graph convolutional network with multiple receptive fields, where a graph generation layer constructs adaptive topological relationships among samples, and deep feature modeling and optimization are performed on the constructed sample topology. Finally, to explicitly optimize the structural consistency and categorical discriminability of the graph feature space, a cross-domain category-aware contrastive learning mechanism is designed. By constructing positive contrastive relationships among cross-domain intra-class samples and negative contrastive relationships among inter-class samples, fine-grained alignment of feature distributions and semantically consistent cross-domain transfer are achieved for samples of the same category from source and target domains. The proposed method achieves average accuracies of 90.67% and 93.67% on the public CWRU and JNU datasets, respectively, representing improvements of 4.68 and 1.69 percentage points over the second-best comparative methods, thereby validating its effectiveness for unsupervised fault diagnosis across multiple variable speed cross-domain transfer tasks.

摘要： 针对变转速工况下的滚动轴承故障诊断中标签稀缺以及特征分布难以细粒度对齐等问题，提出一种耦合时频注意力驱动的图类别感知对比学习方法(CACL)用于无监督跨域诊断。首先，在特征提取方面，为了同时提取故障信号的时域与频域判别特征，并提升对长尾分布与弱故障的敏感性，构建耦合时频注意力模块；然后，将提取的深度判别特征输入到多感受野协同的图卷积网络，利用图生成层构建样本间自适应拓扑关系，并对构建的样本拓扑结构进行深度特征建模和优化；最后，为显式优化图特征空间的结构一致性与类别判别性，设计了跨域类别感知对比学习机制，通过构建跨域同类样本的正对比关系与异类样本的负对比关系，实现源域与目标域同类样本特征分布的细粒度对齐和语义一致的跨域迁移。所提方法在公开的CWRU和JNU数据集上的平均准确率分别为90.67%与93.67%，与对比实验次优方法分别提高了4.68个百分点和1.69个百分点，在多个变转速工况的跨域迁移任务中验证了其无监督故障诊断的有效性。