The R&D&I project of Rover Rail, railway division of Rover Grupo, combines ground-penetrating radar and machine learning to improve the detection of anomalies in railway tracks and move toward more efficient and anticipatory maintenance models
The maintenance of railway infrastructures depends to a large extent on knowledge of the condition of the elements located beneath the rails. These layers support train loads and are fundamental to ensuring the stability, safety and reliability of the track.
With the aim of improving its analysis, Rover Rail has developed SMART RAIL, an innovation project that applies geophysical exploration techniques and machine learning to railway maintenance.
The initiative, developed together with Geozone Asesores and framed within the IVACE+i cooperative R&D project program, seeks to identify possible pathologies in the lower layers of the infrastructure before their evolution requires interventions of greater scope.
The system uses ground-penetrating radar technology and massive data capture through a multiarray team installed on railway machinery. The algorithms then allow the analysis of patterns related to the condition of the ballast and the detection of possible problems of compaction, contamination, settlement or subsidence.
In this way, Rover Rail and Rover Grupo move toward a predictive maintenance model, based on having more information to anticipate actions, prioritize resources and improve the planning of interventions.
SMART RAIL culminated its development in 2024 after completing the field tests, data processing and model validation.
The initiative adds to the operational experience of Rover Rail, which currently maintains more than 5,300 kilometers of railway lines in Spain, more than 2,000 of them in Andalusia, and has its own fleet of more than 35 railway machines.
With projects like SMART RAIL, Rover Grupo reinforces its commitment to innovation applied to infrastructures, combining technical knowledge, field experience and new technologies to contribute to a more reliable, efficient railway system prepared to anticipate maintenance needs.