Handsfree, Fully Autonomous, Plant-Scale Anomaly Detection AI

This paper presents a self-supervised autonomous “plant scale” AI — capable of monitoring every PLC and IIoT parameter of a steel plant — automatically detecting and accelerating diagnosis of anomalies. Automatic anomaly detection proactively informs plant operations of conditions that otherwise would go undetected — leading to informed production and maintenance decision-making. Self-supervised AI overcomes the challenges of constant equipment, environment, and product changes that thwart classical machine learning approaches. Normalized severity scoring of the AI results further enable prioritization of the anomalies for investigation and action. We describe several use cases of this new AI in commercial operation along with the corresponding user workflow.