Digital Twin Technologies for ICS: Leveraging Virtualization and Sensor Data for FAT/SAT, Commissioning and Predictive Risk Detection

The integration of Digital Twin (DT) technologies in industrial control systems (ICS) has emerged as a transformative approach to enhance system design, operation, and risk management. This paper explores the application of DTs by leveraging virtualization techniques and real-time sensor data to optimize Factory Acceptance Testing (FAT), Site Acceptance Testing (SAT), commissioning processes, and predictive risk detection. By creating virtual replicas of physical control systems, operators can simulate, monitor, and validate system behavior under various operational scenarios, enabling proactive identification of faults and anomalies before they affect live operations. The study proposes a structured framework for DT implementation, combining high-fidelity sensor integration, model-based simulations, and AI-driven predictive analytics. Experimental results demonstrate significant improvements in commissioning efficiency, reduced testing time, and early detection of potential system failures, thereby mitigating operational risks. Comparative analysis with traditional ICS testing methods highlights the enhanced accuracy, adaptability, and cost-effectiveness of DT-driven approaches. The findings underline the strategic value of digital twins in advancing industrial automation, operational reliability, and predictive maintenance. Limitations regarding data quality, integration complexity, and computational overhead are discussed to provide a balanced perspective.