Simulation-Driven Digital Twin for Condition Monitoring of Rotating Machinery Under Industry 4.0

Digital Twin systems offer real-time insights into the health and operation of machinery and are recognized as a foundational pillar of Industry 4.0. But, their adoption in small- and medium-scale industries is often constrained by high costs and implementation complexity. This paper presents a simulation-driven Do-It-Yourself (DIY) Digital Twin framework for the condition monitoring of rotating machinery, developed entirely using virtual tools without the need for physical experimentation. The system models an industrial rotating machine in MATLAB/Simulink, simulating operational parameters such as vibration, temperature, and rotational speed under both normal and faulty conditions. Simulated fault scenarios including imbalance, misalignment, and overheating are introduced to evaluate system responses and derive machine health indicators. A virtual dashboard visualizes machine behavior in real time, enabling early anomaly detection and proactive maintenance planning. The proposed approach provides a low-cost, scalable, and accessible solution for integrating Digital Twin technology into predictive maintenance strategies, aligning with the broader objectives of smart manufacturing and Industry 4.0-driven diagnostics.