Digital shift in railway maintenance
Digital innovation is everywhere today on the rail network, where modernisation is crucial to meeting the needs of increasingly intense and sustainable mobility. Innovation is taking place in rolling stock, but also in infrastructure and tracks, the two areas in which ETF specialises. This transformation is having a major impact on our maintenance models./strong>
The digitisation of the railways is at the heart of a vast and complex issue: regenerating and securing a vast ageing infrastructure while preparing the network of the future, which will need to be more efficient and increasingly robust. This balance between modernisation and innovation of the network requires considerable resources from the State, the SNCF and local authorities.
In the Pays de la Loire region alone, for example, SNCF announced an investment plan of more than €1 billion in the digitisation of signal boxes in early 2025. The aim is to increase traffic on the tracks without adding new rail infrastructure. The digitisation of the network should enable more trains to run more punctually – and with ever greater safety – on the same infrastructure, by taking full advantage of connectivity and data centralisation.
BIM: the future of predictive maintenance applied to railways
Data, collected, exploited and shared on a large scale, thus becomes a strategic asset for all operators. Its management applied to maintenance enables the creation of predictive models developed by the two entities of the VINCI Railways Group, LISEA and MESEA, on the LGV Sud Europe Atlantique high-speed line. SE@Cloud marks a new stage in the use of data for railway maintenance.
Its mission is to leverage all the operational data from the Tours-Bordeaux line to meet the new challenges associated with artificial intelligence and predictive maintenance. In concrete terms, SE@Cloud cyclically processes huge amounts of data from the installations themselves (connected objects), on-board monitoring, manual entries and external flows (weather, transport plans, etc.).
SE@cloud automates repetitive processes and can use artificial intelligence to develop digital reports that are useful for maintaining assets: current status of facilities and extrapolations for different time horizons, degradation models, detection of abnormal behaviour, etc.
Operational staff and decision-makers have real-time access to these results via web-based business intelligence tools and geographic information systems. This enables them to adapt maintenance and renewal plans according to the actual condition of the equipment and the traffic supported, and to anticipate failures before they occur through predictive maintenance.
On the Dax–Bayonne line, ETF has designed its first comprehensive 3D model incorporating terrain profiles, tracks and catenary supports. This project serves as a demonstration: it illustrates how data, integrated from the design stage, facilitates future maintenance and traceability.
Today, ETF engineers and data analysts are dreaming of a true BIM, or Building Information Modelling. Initially designed – as its name suggests – for buildings, this model is inspiring railway maintenance. The principle? A digital model that goes beyond 3D and is not limited to a graphical representation of the network. Thanks to BIM, each element of the infrastructure becomes a source of data that can be used for predictive maintenance. The result is a comprehensive, shared vision that is accessible to both engineers and field operators, ultimately leading to better service for end customers: passengers.
MEETING WITH RACHID HARTOUN
Head of overhead line design offices at ETF
BIM: a new dimension in 3D modelling
“BIM is a much more powerful tool than 3D. With 3D, you model the project: you see the position of the supports, rails, catenaries, etc. With BIM, you add several dimensions: time, cost, and the lifespan of the parts. You can find out how much a pole costs, how long it takes to install it, and even plan the entire construction site based on this data. But the real revolution is metadata. For each support, we enter the type of steel, the height of the contact wire, the batch number, the installation date, etc. If a part proves to be faulty, we can find all the places where it has been used in just a few clicks.”
GéoVoie on LGV :
Using data from SE@Cloud, GéoVoie is the digital twin of the track geometry that meets the needs of three areas:
- Short term: meet geometric standards to enable high-speed trains to travel at 320 km/h
- Medium term: schedule heavy mechanical tamping campaigns as accurately as possible, neither too much nor too little
- Long term: reschedule the track renewal programme (RB, RVB) for the LISEA concessionaire
By combining digital innovation, data expertise and practical know-how, ETF is helping to build the railway maintenance model of tomorrow: more predictive and more sustainable.
