Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosis

TL;DR

This paper introduces a novel framework combining knowledge distillation, GCNs with ARMA filters, and enhanced domain adaptation techniques to improve bearing fault diagnosis under resource constraints and varying conditions.

Contribution

It proposes a progressive knowledge distillation method with GCNs and a new discrepancy measure, enhancing fault diagnosis accuracy and robustness across different working conditions.

Findings

Achieves higher diagnostic accuracy on benchmark datasets

Reduces computational costs significantly

Validates effectiveness through comprehensive ablation studies

Abstract

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.