Li Fan, Li Yunfeng, Wang Dongfeng
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Collaborative Innovation Center of Henan Province for High-End Bearing, Luoyang 471003, China.
Sensors (Basel). 2025 Jun 23;25(13):3894. doi: 10.3390/s25133894.
To address the challenge of incomplete fault feature extraction in rolling bearing fault diagnosis under small-sample conditions, this paper proposes a Temporal-Graph Convolutional Fusion Network (T-GCFN). The method enhances diagnostic robustness through collaborative extraction and dynamic fusion of features from time-domain and frequency-domain branches. First, Variational Mode Decomposition (VMD) was employed to extract time-domain Intrinsic Mode Functions (IMFs). These were then input into a Temporal Convolutional Network (TCN) to capture multi-scale temporal dependencies. Simultaneously, frequency-domain features obtained via Fast Fourier Transform (FFT) were used to construct a K-Nearest Neighbors (KNN) graph, which was processed by a Graph Convolutional Network (GCN) to identify spatial correlations. Subsequently, a channel attention fusion layer was designed. This layer utilized global max pooling and average pooling to compress spatio-temporal features. A shared Multi-Layer Perceptron (MLP) then established inter-channel dependencies to generate attention weights, enhancing critical features for more complete fault information extraction. Finally, a SoftMax classifier performed end-to-end fault recognition. Experiments demonstrated that the proposed method significantly improved fault recognition accuracy under small-sample scenarios. These results validate the strong adaptability of the T-GCFN mechanism.
为解决小样本条件下滚动轴承故障诊断中故障特征提取不完整的挑战，本文提出了一种时域-图卷积融合网络（T-GCFN）。该方法通过对时域和频域分支的特征进行协同提取和动态融合来增强诊断鲁棒性。首先，采用变分模态分解（VMD）提取时域本征模态函数（IMF）。然后将这些函数输入到时域卷积网络（TCN）中以捕获多尺度时间依赖性。同时，通过快速傅里叶变换（FFT）获得的频域特征用于构建K近邻（KNN）图，该图由图卷积网络（GCN）处理以识别空间相关性。随后，设计了一个通道注意力融合层。该层利用全局最大池化和平均池化来压缩时空特征。然后，一个共享的多层感知器（MLP）建立通道间依赖性以生成注意力权重，增强关键特征以提取更完整的故障信息。最后，一个SoftMax分类器进行端到端的故障识别。实验表明，该方法在小样本场景下显著提高了故障识别准确率。这些结果验证了T-GCFN机制的强大适应性。
