Anomaly Monitoring of Dynamic Wastewater Regeneration Process Based on Recursive Broad Learning System
Chang Peng, Lu Yan · IEEE Transactions on Automation Science and Engineering · 2025
In the wastewater regeneration process, a condition monitoring system must have dynamic adaptability to new operating conditions while maintaining a lasting adaptability to existing conditions to ensure efficient and stable operation. This paper proposes a Slow Feature Recursive Broad Learning System (SF-RBLS) to achieve incremental learning of new knowledge and sustained adaptation to condition characteristics. The model takes advantage of the incremental learning mechanism of the broad learning system, enabling online learning of unknown features as new operating conditions emerge. An innovative coupling mechanism between the slow feature space and the recursive structure is constructed. Through the slow feature analysis mechanism, temporal constraints are imposed on the dynamic evolution process of the recursive window, enabling the feature extraction process to simultaneously generate long-term steady state representations and short-term dynamic responses, achieving enduring adaptability to various conditions. Experimental results demonstrate that SF-RBLS exhibits significant advantages over state-of-the-art methods in both accuracy and stability for real-time monitoring and anomaly detection, confirming its effectiveness and potential in intelligent wastewater reclamation monitoring.