Digital Twins and Their Impact on Predictive Maintenance in IoT-Driven Cyber-Physical Systems

This research paper explores the transformative role of digital twin technology in enhancing predictive maintenance practices within Internet of Things (IoT)-driven cyber-physical systems (CPS). As industries increasingly adopt IoT solutions, the complexity and interconnectivity of physical assets necessitate innovative maintenance strategies aimed at minimizing downtime and optimizing operational efficiency [1]. Digital twins, which are virtual representations of physical entities, facilitate real-time monitoring, simulation, and predictive analytics by leveraging data from connected sensors [4][34].This study first provides a comprehensive overview of the digital twin concept, detailing its architecture, integration with IoT frameworks, and capabilities for data assimilation and analytics. It then delineates the relationship between digital twins and predictive maintenance, underscoring how these virtual models can predict equipment failures by analysing historical and real-time operational data [2][10].