In this presentation, we showcase a novel methodology for automated strip break classification that uses Falkonry’s time series AI platform to classify the complex waveforms of tandem mill parameters associated with strip breaks. Strip breaks are unwanted yet common occurrences during cold rolling of steel. Strip breaks have various causes, such as weld breaks, material defects, or steering issues, and result in lost productivity, material scrap, and equipment damage. Today, analysis of strip breaks requires manual extraction of multiple time series parameters from SCADA systems or historians and extremely resource and time-intensive human interpretation of these parameters just before the strip break event. The resources and time required to analyze strip breaks are expensive, but more expensive is the lost time between events and diagnosis. Breaks are temporally indiscriminate. Human analysts are neither on-call nor capable of near-instant analysis — which is required for production teams to determine causes and actions in response to strip breaks. Automated classification allows low-latency automated classification of strip breaks for use by operations teams to understand underlying causal factors and implement corrective actions.
Conduct a free, 1-week offline trial using your historical Parquet data. No discussions on plant design or business objectives required.
