Continuous Learning Method for Incremental Fault Class Identification of Control Valves Based on Weighted Supervised Contrastive Knowledge Distillation

Data-driven methods have achieved high accuracy in diagnosing control valve faults. However, incremental learning (IL) methods for step signal fault diagnosis of control valves remain insufficient. First, the distinct characteristics of various step-response signals lead to data imbalance, which makes it difficult for models to extract discriminative features from step-fault data. Second, the arrival of a novel fault class necessitates full retraining and incurs prohibitive computational overhead. Finally, as the number of fault classes increases, the model tends to bias toward newly added fault classes and may suffer from catastrophic forgetting of previously learned ones. These issues collectively degrade the performance of incremental fault diagnosis models. To solve these problems, this article proposes an incremental fault diagnosis model based on weighted supervised contrastive knowledge distillation (WSCKD), which extends the classical incremental classifier and representation learning (ICARL) framework in three key ways. First, supervised contrastive learning (SCL) is weighted to enhance representation learning for step-response signals. In addition, a new preferred example selection method, greedy diversity sampling (GDS), is proposed to limit computational overhead and is combined with knowledge distillation to mitigate catastrophic forgetting. Furthermore, a balanced bagging classifier (BBC) alleviates the adverse effects of class imbalance. Extensive simulation experiments were conducted on a control valve system. Experimental results demonstrate that the WSCKD method outperforms existing approaches, achieving average incremental accuracies of 94.32% for five fault classes.