Nowadays, about 50 percent of the life cycle costs of railway infrastructures are made up by maintenance costs. In addition, rising demands on railroad infrastructure operators by means of profitability and punctuality call for advanced concepts of Prognostics and Health Management.

Condition-based preventive maintenance aims at strengthening the rail mode of transport through an optimized scheduling of maintenance actions based on the actual and prognosticated infrastructure condition. Prerequisite therefore is the almost continuous condition monitoring for thousands of kilometers of railway tracks as well as ten thousands of technical systems and sub-systems. The rapidly expanding possibilities for embedded sensors in all types of technical components as well as in-line railway vehicles are the key enabler for condition-based preventive maintenance in large and distributed railway networks. This Special Issue solicits papers that discuss the development of advanced sensor-based condition monitoring, smart data management, intelligent diagnostic data analysis, degradation models, condition prognosis and maintenance scheduling for railway systems. All systems concerned that will benefit from PHM are of interest such as infrastructure (track geometry/rail condition, point machines), rolling stock (brake pads, diesel engines, traction motors, wheel health, real-time monitoring /event analysis), signaling equipment, etc.