Explorative hybrid digital twin framework for predictive maintenance in steel industry
Sotirios Panagou, Fabio Fruggiero, Carmen Del Vecchio, Kisan Sarda, Fernando Menchetti, Luca Piedimonte, Oreste Riccardo Natale, Salvatore Passariello
Abstract
Manufacturing systems in steel industries are characterized by high complexity and high temperature and pressure conditions during production. Industries have to speed up their production to meet the market's demand for products in a fast changing economy. To prevent breakdowns in the manufacturing lines and further economic loss, steel industries utilize preventive maintenance approach and early replacement of equipment, which is expensive and not optimal. Preventive maintenance can be beneficial in the steel industry and reduce costs, if it is supported by information gathered from previous breakdowns in the production line, such as condition of equipment, environment and further data that can be collected. In this work, historical data and data collected from a digital twin representation of the manufacturing line from Pittini, a steel making industry in Italy, were utilized to gain information on the conditions before a breakdown in the production line. Furthermore, we present a cloud based framework created by utilizing the information and data for optimization and real-time driven preventive maintenance approach and remote control.