Abstract

Railway asset change detection through Artificial Intelligence (AI) technology has transformed infrastructure monitoring by providing better efficiency combined with predictive maintenance functions and improved accuracy. The paper studies the development of railway asset monitoring throughout history while it moved from traditional manual inspections to AI-powered solutions. The study recognizes three main barriers which include irregular data acquisition practices along with restricted sensor abilities and imprecise AI model precision and difficulties applying them to current railway control platforms. The research investigates novel data collection approaches besides studying hybrid AI model deployment and finding optimal solutions for railway operational AI integration. The research findings establish critical requirements for railway administrators and policy makers and developers of AI systems regarding standardized regulatory structures and enhanced AI interpretation capabilities and collaborative advancements. Music research will further AI railway asset monitoring by integrating edge computing technology and 5G connectivity to process real-time data streams. The presented study enhances railway infrastructure management systems through its advances in AI methodology development along with solutions to implementation hurdles.