Development of an Operational Digital Twin of a Freight Car Braking System for Fault Diagnosis

In the evolving landscape of digital technology, the concept of digital twinning has emerged as one of the most important innovations. Digital twinning, characterized by a one-to-one correlation with physical structures in a digital environment, has become an important tool in transferring physical dynamics into digital replicas. This platform, integrating advanced sensor technologies and analytical tools, enables a comprehensive monitoring, diagnosis, and optimization concept. The present research focuses on the application of digital twinning in addressing the complex challenge of managing fault types in freight car brakes. A Digital Twin (DT) model of a freight car braking system is developed that forms the basis for an innovative approach to fault classification. This approach leverages the ability of the DT to emulate and analyze the behavior of the braking system under different conditions. The effectiveness of the proposed method is evaluated through extensive testing and analysis with an experimental data set. The results show that the method is able to accurately diagnose different types of faults. This represents a significant advance in the field of freight car brake system diagnosis. This study presents a digital twin (DT) for freight car brake systems that improves fault diagnosis. By integrating advanced sensors and analysis tools, the DT goes beyond traditional diagnostics and enables accurate fault classification and real-time analysis of system behavior. Extensive testing confirms its accuracy in detecting various faults and heralds a new era of maintenance efficiency. image