Digital Twin of Airport Departure Systems for Anomaly Detection and Trend Prediction

As the importance of departure equipment in modern aviation transportation continues to grow, ensuring its proper operation and promptly identifying potential anomalies have become critical. This study proposes a novel approach leveraging digital twin technology for real-time anomaly detection and future performance prediction of airport departure equipment. By constructing digital twin models, the operational states of the equipment can be dynamically monitored in real time. Combining machine learning and time-series analysis, historical equipment data is used to train the model, enabling accurate anomaly detection and trend prediction. Additionally, the digital twin model forecasts the future states of the equipment, providing valuable data for maintenance planning and resource allocation. Experimental results demonstrate that this method significantly enhances anomaly detection accuracy, minimizes the impact of equipment failures, and offers reliable decision support for airport departure operations. This study not only provides new insights into the intelligent management and maintenance of airport equipment but also serves as a reference for applying digital twin technology to complex systems.