IFD-BiC: a class-incremental continual learning method for diesel engine fault diagnosis

Abstract In recent years, deep learning-based fault diagnosis methods for diesel engines have been developed assuming that a comprehensive dataset is available for one-time learning. However, in reality, it is impractical to obtain a complete dataset all at once. Instead, new data is gradually acquired over time. To address this issue, this paper proposes an incremental fault diagnosis method with bias correction (IFD-BiC). When receiving incremental fault data, the IFD-BiC model incorporates new classifier nodes and updates network parameters using a combination of distillation loss and cross-entropy. During the update process, old samples saved in the exemplar set are replayed, and a bias correction layer is used to correct the prediction bias caused by the imbalance between new and old samples. This enables the model to learn new fault tasks without experiencing catastrophic forgetting. To ensure the applicability of the method to various operating conditions of diesel engines, cyclic angular vibration with consistent length at different speeds is used as the model input. Through experimental validation across a pair of distinct diesel engine models, the proposed method achieves highly accurate fault diagnosis on stream format training data. Moreover, an evaluation against leading incremental learning techniques affirms the proposed method’s advantage.