Research paper is accepted by IEEE Transactions on Reliability.
Title: A Class-Aware Supervised Contrastive Quadratic Neural Network for Imbalanced Bearing Fault Diagnosis
Authors: Wei-En Yu, Shiping Zhang, Jinwei Sun, Jing-Xiao Liao, Xiaoge Zhang
Abstract: Deep learning shows great potential for bearing fault diagnosis, but its effectiveness is severely limited by the prevalent issue of highly imbalanced data in real-world industrial settings, where fault events are extremely rare. This paper proposes a novel method for imbalanced bearing fault diagnosis that combines class-aware supervised contrastive learning with a quadratic network backbone. This integrated approach, named CCQNet, is designed to counter the effects of highly skewed data distributions by improving feature representation and classification fairness. Comprehensive experiments show that CCQNet substantially outperforms existing methods in handling imbalanced data, particularly at high imbalance ratios like 50:1. This study provides an effective and innovative solution for imbalanced bearing fault diagnosis. Source codes of this paper are available at https://github.com/yuweien1120/CCQNet for public evaluation.
