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.

本文主要解决在现代工业环境中，传统故障诊断方法无法有效处理新类型故障和动态条件的问题。提出的SRTFD框架通过回顾核心集选择、全球平衡技术和基于置信度及不确定性驱动的伪标签学习，增强了在线持续学习的能力。实验结果表明，SRTFD能够提供先进且精确的故障诊断解决方案，显著提升系统的安全性和经济效益。

可扩展的实时故障诊断通过在线持续学习