Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach

Within the frame of the Fourth Industrial Revolution, the application of Internet of Things technologies makes it possible to convert conventional manufacturing systems into cyber-physical systems, where the used new technologies enable the improvement of maintenance and operation processes. The aim of this work is to develop and validate a new real-time maintenance policy model and optimization algorithm based on digital twin simulation. The maintenance policy model is based on the conventional failure and operation data from ERP (enterprise resource planning) and the real-time and forecasted failure and operation data from digital twin simulation. The described maintenance policy model and its optimization algorithm represent an innovative way to manage predictive, preventive, corrective, and opportunistic maintenance strategies. The novelty of the presented method is that the real-time data generated by the digital twin solution allow the definition of a more accurate maintenance strategy. The optimization algorithm is characterized by a standard evolutionary algorithm. The impact of maintenance policy optimization on the energy efficiency and emission was analyzed in the case of both conventional and cyber-physical manufacturing systems with and without digital-twin-based simulation. The results showed that the energy consumption and the greenhouse gas emission in the real-time maintenance policy optimization scenario decreased by 21%, depending on the electricity generation source.