GraphSAGE with contrastive encoder for efficient fault diagnosis in industrial IoT systems

Abstract

Imbalanced datasets are common in industrial internet of things (IIoT) systems due to challenges in acquiring faulty labels. Augmentation and graph-based methods have been proposed to improve classification accuracy of deep learning-based systems. However, the conventional approaches can be limited by training complexity and inefficient memory usage. In this paper, GraphSAGE with contrastive encoder (GCE) is proposed to improve classification accuracy and memory utilization efficiency. From the simulation results, it is confirmed that the GCE can improve classification accuracy by up to 23% compared to conventional approaches.

Keywords

• Imbalanced dataset
• Fault diagnosis
• Graph neural network
• Contrastive learning