In recent years, railway systems have played a significant role in transportation systems due to the demand increase in conveying both cargo and passengers. Due to the harsh environments and severe loading conditions, caused by the traffic growth, heavier axles and vehicles and increase in speed, railway tracks are at risk of degradation and failure. Condition monitoring has been widely used to support the health assessment of civil engineering structures and infrastructures. In this context, it was adopted as a powerful tool for an objective assessment of the railway track behaviour by enabling real-time data collection, inspection and detection of structural degradation. According to relevant literature, a number of sensors can be used to monitor track behaviour during the train passing under harsh environments. This paper presents a review of sensors used for structural monitoring of railway track infrastructure, as well as their application to sense the performance of different track components during extreme events. The insight into track monitoring for railways serving traffic with extreme features will not only improve the track inspection and damage detection but also enable a predictive track maintenance regime in order to assist the decision-making process towards more cost-effective management in the railway industry.

Bogies are key subsystems for rolling stock safety and, therefore, meaningful and objective data concerning their condition is of paramount importance for railway operation. These subsystems experience severe service conditions causing wear, damage and degradation of components and affecting the vibrations to which the passengers are exposed. As such, safe and reliable operation, together with a high level of comfort for the passengers, can only be assured by an in-depth, data-based and comprehensive maintenance of the bogie components. In this perspective, advanced health monitoring of the running gear plays a fundamental role as the enabler for condition-based maintenance strategies. This paper reports about work performed in the RUN2Rail project aimed at formulating new concepts for the condition monitoring of the running gear. Three case studies are addressed: wheelsets, powertrain and suspension components. For these cases, the suitable choice and location of sensors is investigated and innovative fault detection and fault classification methods are proposed and preliminarily validated by means of numerical experiments and laboratory tests. A concise outline of the impacts and benefits of each proposed condition monitoring application is also provided.