Smart Rail

The SMART RAIL project developed an artificial intelligence-based tool for the advanced diagnosis of the condition of ballasted railroad tracks.

 

The main objective of the project was to implement a system for the geophysical exploration of the railroad substructure using non-invasive ground-penetrating radar (GPR) techniques, based on machine learning processing of large volumes of data, in order to analyze the degree of ballast contamination.

 

Data collection was carried out using a GPR+GPS+VIS multi-array system installed on a Rover Rail railway maintenance machine, which recorded the data during its route for subsequent processing.

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This approach made it possible to analyze massive and complex sets of geophysical data and detect anomalies in ballast compaction and contamination levels, as well as identify defects or subsidence in the subgrade. The analysis was based on the graphical and numerical correlation of potential patterns detected in the data (more than 500 variables), relating them to the actual condition of the track sections analyzed using Big Data.

 

In this way, we can apply intelligent predictive maintenance and issue early warnings about track sections requiring localized intervention, thereby anticipating the onset of defects and high levels of deterioration throughout the infrastructure’s service life.

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