Contrastive-Based Bi-Update Class Incremental Model and Its Application in Fault Diagnosis

Numerous methodologies have been proposed to tackle the pervasive issue of catastrophic forgetting. However, most current methods tend to segregate the historical and current information during the training process. In contrast, we acknowledge the significance of global properties throughout the entire incremental learning (IL) process to consolidate memory. We propose a feature-level bi-update mechanism that facilitates concurrent learning and revision by fostering mutual awareness between learning and revising. To learn new knowledge, we design a cross-session contrastive learning (CSCL) function. This function benefits from contrastive learning (CL), facilitating the acquisition of robust representations and inherently avoiding collapse. Notably, it accomplishes this without necessitating asymmetric network architectures or relying on data augmentation techniques. To revise prior knowledge, we propose a prototype update approach. This method involves the dynamic interaction between previous semantic representations and the ongoing learning process, thus aligning decision boundaries and promoting coherence. In addition, a novel classification method compatible with the aforementioned objectives is introduced. Extensive experiments have been conducted to validate the proposed method, and the results show that the proposed method reaches new state-of-the-art levels.