Imbalanced Fault Diagnosis by Supervised Contrastive Learning

Peng Peng, Xinyue Wang, Jiaxun Lu, Qi Li, Shuting Tao, Zixuan Wang, Hongwei Wang, Heming Zhang

Abstract: Intelligent fault diagnosis is essential to guarantee the safe operation of industrial processes. And an important issue is how to develop a method to tackle the dilemma where we can only collect limited fault samples. In this paper, we propose a two-stage method based on supervised contrastive learning for imbalanced fault diagnosis tasks. We utilize the supervised contrastive learning technique as it has shown a powerful representation learning ability in previous works. The computational experiments on the Tennessee Eastman dataset show that our proposed two-stage method can achieve improved performance when compared to existing methods.