Research on the Intelligent Maintenance Technology System for Heavy-haul Railway Track Infrastructure Enabled by Scientific and Technological Innovation

Abstract

Heavy-haul railways impose significantly higher demands on track infrastructure maintenance compared to conventional passenger-freight mixed lines due to increased axle loads, extended transport distances, and high-density operational patterns. Traditional approaches, characterized by periodic manual inspections and scheduled routine maintenance, face escalating challenges in safety assurance, cost efficiency, and defect detection accuracy. To address these challenges, this paper develops a comprehensive intelligent maintenance technology system for heavy-haul railway track infrastructure enabled by scientific and technological innovation. The proposed system framework encompasses four interconnected functional layers: multi-source perception, data fusion and management, intelligent evaluation and decision-making, and precision implementation. The perception layer integrates comprehensive track inspection vehicles, unmanned aerial vehicle-based aerial imaging, and fixed sensor networks to achieve all-directional, all-weather, and full-element state monitoring. The management layer employs BIM and GIS technologies to construct digital twin models and full-lifecycle management platforms for tracking infrastructure. The decision-making layer applies artificial intelligence algorithms such as fuzzy comprehensive evaluation and deep learning to develop predictive models that enable a paradigm shift from scheduled maintenance to condition-based maintenance. The implementation layer encompasses digitalized and intelligent maintenance equipment. This systematic architecture effectively addresses the core technical gap in track infrastructure intelligent maintenance, offering both theoretical guidance and practical pathways for the intelligent transformation of heavy-haul railways in alignment with the national strategy for digital and intelligent development within the railway sector.