Prioritized Anomaly Management in Steel Production Using Self-Supervised AI

Due to the unavailability of highly curated data, manufacturers are increasingly adopting anomaly detection to analyze large volumes of operational data in real-time. It is very difficult to review every anomaly manually because of limits on available manpower. This paper, therefore, presents a novel approach to automatically prioritize anomalies to direct human attention to where it is most needed. Using anomaly severity, anomaly persistence, signal importance, anomaly spread, and contextual information, the automated anomaly detection AI enables prioritization of attention to review the critical anomalies for timely diagnosis and corrective actions. We present practical cases of deploying this approach in line-scale steelmaking operations.