Railway Transport infrastructures are one of the most important factors for a country’s progress, and is considered as the “lifeline” of a nation, and is proven as a key element for adding speed and efficiency to a country’s progress. Railways are currently experiencing higher demands on infrastructure performance, capacity and service quality. As a result, higher level of resilience against failure, robustness and availability at reduced cost are expected, to meet the planned capacity utilization and safety criteria. Track is the fundamental part of railway infrastructure and represents a significant part of maintenance effort and cost. Track system (e.g. rails, wheels, switches and crossings) degrades with age and usage; and loses its functionality over time. Poor quality of track, may result in e.g. safety problems, speed reduction, traffic disruption, greater maintenance cost. Hence, it is of outmost importance to develop methodologies and tools for accurate prediction of track condition, as well as selection and implementation of an effective health management strategy. The aim of this special issue is to publish state-of-the-art research papers focused on the topics associated with data quality, fault diagnosis, prognosis and health management, as well as Life cycle assessment, maintenance planning and scheduling of railway track system. Analytical models, empirical studies, and case studies are all welcomed as long as an article provides new insights and implications to the field of Railway track life cycle and health management.
Keywords: Railway track; Machine learning; Predictive analytics; Data quality; Fault diagnosis, prognosis and health management; Life cycle assessment; Maintenance planning and scheduling.