The paradigm transition of the life cycle management of physical assets in the railway sector demands new maintenance models that imply the conventional predictive approaches to be surpassed. This paper proposes an innovative methodology that integrates Building Information Modelling (BIM) with predictive maintenance (PdM) systems to be applied to rolling stock and, in this way, be enhanced by Generative Artificial Intelligence (GenAI). The research focuses on the autonomous synchronisation of the Rolling Stock Digital Twin (DT). Unlike static BIM models, the proposed solution enables the use of GenAI algorithms to process continuous data streams from integrated sensors, allowing the digital model to evolve autonomously as physical wear occurs. In this framework, GenAI (via Generative Adversarial Networks—GANs) is essential for data augmentation, enabling the simulation of rare “long-tail” failure events that are scarce in real-world historical data. By synthesising these degradation scenarios, the model learns complex mechanical collapse patterns that otherwise would be ignored by traditional PdM approaches. GenAI is employed to synthesise degradation scenarios, perform real-time parametric updates within the IFC (Industry Foundation Classes) schema, and optimise maintenance workflows. The application of this framework demonstrates a significant reduction in diagnostic latency and optimises the rolling stock’s operational life cycle by automating updates and reducing the need for manual data entry. This study concludes that the convergence among BIM, PdM, and GenAI establishes a robust framework for railway fleet management. While the current validation focuses on bogie systems using Random Forest and LLMs, it paves the way for a future Industrial Metaverse where immersive diagnostics can be integrated into the maintenance lifecycle.
Coutinho et al. (Mon,) studied this question.