Advancing Rail Infrastructure: Integrating Digita Twins and Cyber-Physical Systems for Predictive Maintenance

The railway sector struggles with infrastructure inefficiencies due to traditional maintenance methods, resulting in high costs and unplanned disruptions. This paper provides a conceptual framework that integrates Digital Twins (DT) and Cyber-Physical Systems (CPS) to enhance predictive maintenance (PdM). By establishing a technical architecture for real-time monitoring and fault detection, the framework will facilitate seamless data exchange for automated decision-making. The findings contribute to advancing intelligent railway maintenance, fostering sustainability, and enhancing resilience in railway operations. This research provides actionable insights for industry stakeholders, supporting the transition towards data-driven, adaptive maintenance strategies in modern rail networks.