Semisupervised Machine Fault Diagnosis Fusing Unsupervised Graph Contrastive Learning

Abstract

By learning effective information from unlabeled nodes, node-level graph data-driven diagnosis methods perform better than graph-level methods. However, features of unlabeled nodes, indirectly involved in graph feature learning, are not fully utilized. To overcome above limitations, a semi-supervised machine fault diagnosis fusing unsupervised graph contrastive learning (GCL) is proposed. A new GCL framework, where positive and negative graphs are generated by calculating Pearson correlation coefficient, is fused into the graph transformer network (GTN). Further, a new combined loss, including a supervised cross-entropy loss and a new unsupervised GCL loss, is designed for GTN training. Contrastive learning of positive and negative graphs is guided by the unsupervised GCL loss. While the semi-supervised graph feature learning for original graphs is mainly driven by the supervised cross-entropy loss, where the GTN for graph feature learning shares parameters. Experimental results on public and real datasets show the proposed method achieves a competitive performance.