A Data-Driven Framework for Optimized System Maintenance

System maintenance is crucial to ensure the safety, reliability, and performance of modern systems. Effective maintenance reduces downtime, prevents unexpected failures, and extends the lifespan of equipment. With continuing monitoring though the deployment of digital twins we can develop new data driven approaches that are more efficient than traditional maintenance policies. Digital twin technology provides a real-time virtual model of a physical system, enabling continuous monitoring, diagnosis, and advanced analytics. In this study, we propose a framework combining predictive and prescriptive pipelines, utilizing machine learning techniques to tackle optimization problems. Specifically, we explore methods such as Smart Predict-then-Optimize (SPO) and we compare with the traditional approaches Predict-then-Optimize (PTO) for system maintenance. Different maintenance policies, including Condition-Based Maintenance (CBM), periodic CBM, and predictive maintenance, are applied within this framework. An illustrative example of battery maintenance demonstrates the practical implementation of these methodologies. By leveraging data-driven approaches, this framework enhances decision-making and helps prevent costly disruptions in critical systems.

Keywords: Smart-predict-then-optimize, Artificial intelligent, Digital twin, System maintenance, Industry 4.0/5.0 reliability/safety, System health management.