Predictive Maintenance of Railway Infrastructure Using Artificial Intelligence

Abstract

The increasing complexity and safety requirements of global railway networks require a transition from traditional manual inspections to advanced data-driven maintenance strategies. This article synthesizes recent advancements in the integration of Artificial Intelligence and Machine Learning for the predictive maintenance of railway infrastructure. It explores a diverse range of methodologies, including Long Short-Term Memory networks for sequential forecasting, Graph Convolutional Network for spatial track analysis, and mobile LiDAR technology for high-precision 3D monitoring. Research findings indicate that these AI-driven approaches can achieve prediction accuracies exceeding 99%. Furthermore, the integration of Deep Reinforcement Learning with Digital Twins has been shown to reduce maintenance activities by 21% and occurring defects by 68%, significantly enhancing operational efficiency. Despite these successes, the article highlights the challenge posed by the “black-box” nature of complex models in safety-critical environments. To address this, the role of eXplainable AI (XAI) frameworks, such as SHAP and LIME, is analysed as a crucial component for fostering trust and ensuring compliance with stringent safety standards like EN 50128. The synthesis concludes that the future of railway management lies in the synergy between robust 3D data acquisition and model interpretability.