SRTFD: Scalable Real-Time Fault Diagnosis through Online Continual Learning

Fault diagnosis (FD) is essential for maintaining operational safety and minimizing economic losses by detecting system abnormalities. Recently, deep learning (DL)-driven FD methods have gained prominence, offering significant improvements in precision and adaptability through the utilization of extensive datasets and advanced DL models. Modern industrial environments, however, demand FD methods that can handle new fault types, dynamic conditions, large-scale data, and provide real-time responses with minimal prior information. Although online continual learning (OCL) demonstrates potential in addressing these requirements by enabling DL models to continuously learn from streaming data, it faces challenges such as data redundancy, imbalance, and limited labeled data. To overcome these limitations, we propose SRTFD, a scalable real-time fault diagnosis framework that enhances OCL with three critical methods: Retrospect Coreset Selection (RCS), which selects the most relevant data to reduce redundant training and improve efficiency; Global Balance Technique (GBT), which ensures balanced coreset selection and robust model performance; and Confidence and Uncertainty-driven Pseudo-label Learning (CUPL), which updates the model using unlabeled data for continuous adaptation. Extensive experiments on a real-world dataset and two public simulated datasets demonstrate SRTFD's effectiveness and potential for providing advanced, scalable, and precise fault diagnosis in modern industrial systems.