Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support

One of the main benefits of the railways digital transformation is the possibility of increasing the efficiency of the Asset Management process through the combination of data-driven models and decision support systems, paving the road towards an Intelligent Asset Management System (IAMS). The paper describes the whole IAMS decisional process based on a real railway signaling use case: from field data acquisition to decision support. The process includes data collection, preparation and analytics to extract knowledge on current and future assets' status. Then, the extracted knowledge is used within the decision support system to prioritize asset management interventions in a fully-automated way, by applying optimization logics and operational constraints. The target is to optimize the scheduling of maintenance activities, to maximize the service reliability and optimize both usage of resources and possession times, avoiding (or minimizing) contractual penalties and delays. In this context, a real use case related to signaling system and, in particular, to track circuits, is presented, applying the proposed methodology to an Italian urban rail network and showing the usefulness of the approach and its possible further developments.