VMAP-Analytics

Purpose and goal

A large amount of data is currently collected from the rolling mill from an array of sensors and is stored in proprietary databases. A substantial effort is needed in developing customized solutions for analyzing these vast data. VMAP-Analytics aims to create new digital twins (DTs) and establish a standard for interoperability between sensor data and DTs to enhance the integration between physical and virtual processes. This would enable a vendor-neutral integration of analytics software to optimize product quality, ensure the process´s robustness, and monitor the plant.

Expected results and effects

Sensors and DTs can provide insights into the root causes of the production problems, minimizing variations, and reducing scrap. Analytics can be utilized in making the process more robust. DTs and analytics can assist in exploration, testing assumptions about the production process. Machine learning algorithms applied to production data detect correlations and form predictions about the remaining useful life of assets. Analytics tools allow combining live and historical data providing insights and perspective.

Planned approach and implementation

Digital twins (DTs) are process models that are continuously updated with sensor data. VMAP-Analytics will bring together sensors and DTs into one platform by extending the existing VMAP-1 standard. This approach will highlight the potential gaps in sensors and data. The project will also develop a demonstrator platform that will implement analytics and machine learning to improve product quality, improve the robustness of production, condition monitoring for the mill, and visualize the process´s state.