用图卷积网络提升故障诊断精度，同时降低资源消耗。
Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosis
- 通过知识蒸馏将复杂模型知识迁移到轻量学生模型
- 在CWRU和JNU数据集上准确率更高，计算成本显著降低
- 适合工业场景中资源受限的设备故障诊断
在工况变化条件下进行轴承故障诊断面临标注数据不足、分布差异大及资源受限等挑战。为此，我们提出一种渐进式知识蒸馏框架，利用带有自回归移动平均滤波器的图卷积网络（GCN-ARMA）从复杂教师模型向紧凑高效的学生模型迁移知识。为缓解分布差异与标签不确定性，引入增强型局部最大均值差异（ELMMSD），在再生核希尔伯特空间（RKHS）中结合均值与方差统计，并融入标签先验分布，有效拉大聚类中心距离，弥合子域间隙，提升子域对齐可靠性。在基准数据集CWRU和JNU上的实验表明，该方法在显著降低计算开销的同时实现更优诊断精度。全面的消融实验验证了各组件的有效性，证明该方法在多种工况下具备强鲁棒性和适应性。
原文摘要 · Abstract (English)
Bearing fault diagnosis under varying working conditions faces challenges, including a lack of labeled data, distribution discrepancies, and resource constraints. To address these issues, we propose a progressive knowledge distillation framework that transfers knowledge from a complex teacher model, utilizing a Graph Convolutional Network (GCN) with Autoregressive moving average (ARMA) filters, to a compact and efficient student model. To mitigate distribution discrepancies and labeling uncertainty, we introduce Enhanced Local Maximum Mean Squared Discrepancy (ELMMSD), which leverages mean and variance statistics in the Reproducing Kernel Hilbert Space (RKHS) and incorporates a priori probability distributions between labels. This approach increases the distance between clustering centers, bridges subdomain gaps, and enhances subdomain alignment reliability. Experimental results on benchmark datasets (CWRU and JNU) demonstrate that the proposed method achieves superior diagnostic accuracy while significantly reducing computational costs. Comprehensive ablation studies validate the effectiveness of each component, highlighting the robustness and adaptability of the approach across diverse working conditions.
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