Scalable Machine Learning for Real-Time Fault Diagnosis in Industrial IoT Cooling Roller Systems

The CISDI Hot Rolled Strip Cooling Roller Health Monitoring System (HRSCR-HMS) is a platform widely deployed in hot rolled steel manufacturing, providing real-time monitoring, fault diagnosis, and health management for cooling rollers. Operating in industrial IoT environments with high data velocity and volume, the system faces challenges in scaling real-time fault diagnosis due to evolving fault patterns, severe data imbalance, and the need for both diagnostic accuracy and efficiency. Evaluations of state-of-the-art fault diagnosis (FD) methods, including online continual learning (OCL) algorithms like Camel, reveal their limitations in meeting the real-time adaptability and data processing demands of HRSCR-HMS. To address these challenges, we propose SRTFD, a scalable framework tailored for real-time fault diagnosis in industrial IoT systems. SRTFD processes high-velocity data streams using three core innovations: Retrospect Coreset Selection (RCS) for optimizing training efficiency by reducing redundant data, Global Balance Technique (GBT) for robust performance with imbalanced data streams, and Confidence and Uncertainty-driven Pseudo-label Learning (CUPL) for adaptive updates with unlabeled data. Experiments on industrial datasets demonstrate that SRTFD outperforms competing methods, addressing challenges of imbalance, redundancy, and scalability. Integration within HRSCR-HMS validates SRTFD as a practical, scalable solution aligned with the stringent demands of stream data processing in industrial IoT environments.