Incremental Few-Shot Fault Diagnosis With Cosine-Represent Real-Time Evolved Network of Transmission Systems
Shen Yan, Haidong Shao, Zhuo Long, Bin Liu
Abstract
Transmission systems is prone to developing new faults over long-term operations. Incremental learning offers a solution by continuously acquiring new knowledge from online data streams while preserving existing knowledge, making it ideal for ongoing diagnosis in industrial settings. However, the scarcity of incremental fault data and the time-intensive updating of incremental diagnosis methods reduce the effectiveness of existing models in real-time incremental diagnosis. To address this issue, a Cosine-represent Real-time Evolved Network (CREN) is proposed to cope with the challenge of incremental few-shot fault diagnosis (IFFD) for transmission systems. First, a cosine-represented learning strategy is employed to achieve bias imputation in few-shot scenarios by redesigning the mapping between features and weights. Second, a continuous evolution classifier is constructed to achieve real-time incremental updating by embedding decoupled nonparametric class prototypes into the classification weights. The efficiency of the proposed method is verified through IFFD experiments on the two transmission systems.