A Class-Aware Supervised Contrastive Quadratic Neural Network for Imbalanced Bearing Fault Diagnosis

Deep learning holds significant potential for bearing fault diagnosis; however, its effectiveness is often hindered by the pervasive issue of imbalanced data in industrial settings, where fault events are inherently rare. To address this widespread challenge, we propose the class-aware supervised contrastive quadratic neural network (CCQNet), a novel framework combining a class-aware supervised contrastive learning scheme with a quadratic neural network backbone. Our approach introduces two key components to tackle data imbalance: a class-weighted contrastive loss and a logit-adjusted cross-entropy loss, which work in tandem to ensure the model pays equal attention to both majority and minority classes. In addition, we enhance feature extraction through a quadratic convolutional residual network, and provide a novel theoretical analysis linking the function of the quadratic neuron to the principle of autocorrelation in signal processing. Comprehensive experiments on both public and proprietary datasets demonstrate that CCQNet substantially outperforms state-of-the-art methods, particularly in scenarios with extreme data imbalance.