International Journal of Science, Engineering and TechnologyAuthors: Pawar Aditya Mahendra, Prof. M. R. Kumbhoje, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Railways move enormous numbers of passengers and freight, and their safety and punctuality depend on the condition of tracks, rolling stock and signalling and on effective scheduling. Manual inspection and fixed maintenance cycles struggle with the scale of modern networks and with the need to detect defects before they cause accidents or delays. This paper reviews Artificial Intelligence applications across railway systems. It examines deep convolutional networks and object detectors for automated track and rail-surface defect detection from inspection images, recurrent networks for vibration-based condition monitoring of axle-box bearings and other rolling-stock components, big-data analytics and machine learning for train delay prediction, and optimization and learning methods for real-time rescheduling after disruptions. Automatic train protection, including India's Kavach system, and edge deployment of compressed models on inspection vehicles are discussed. A five-layer architecture comprising multimodal sensing, edge inference, a railway data platform, AI decision support and operations control is proposed, with research gaps, objectives, a comparison of applications and challenges including harsh environments, rare failure data, safety certification and integration with legacy systems. The review concludes that AI can make railways safer, more reliable and more efficient when deployed within rigorous safety frameworks.
DOI: https://doi.org/10.5281/zenodo.24214730
