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EU-Rail at InnoTrans 2026 — Highlights of the Day 1 (22 September)
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Data-Driven Solutions for Smarter Rail Maintenance
Challenge
Keeping trains available, reliable and safe is a constant challenge for the European railway sector. Condition-Based Maintenance (CBM) offers a way forward by using asset condition data to determine when and what maintenance is needed. However, implementing this approach across different fleets is not straightforward. Data sources, onboard systems and rolling stock platforms can vary significantly, making it essential to develop solutions that are scalable and capable of working in heterogeneous operational environments.
The solutions
Against this backdrop, Europe’s Rail (EU-Rail) has developed two complementary solutions that harness digital technologies, monitoring and data analytics to enable a more intelligent, data-driven approach to maintenance.
Health Monitoring & Analytics of HVAC & Brake systems
RENFE Fleets (Spain)
The solution enables condition-based monitoring of HVAC and compressed air systems through the exploitation of existing onboard data sources, without the need for additional hardware installation or sensor retrofitting.
It implements a complete data value chain, including onboard data acquisition, off-board data transfer, cloud-based processing and analytics, and integration into maintenance decision-making processes.
The approach has been applied across multiple rolling stock platforms, with analytics independently developed for S103 by RENFE and for S106 by TALGO, demonstrating its applicability in heterogeneous, non-standardised data environments.
Readiness for Industrialisation and Deployment
The solution is based on DSS and HMI and is currently within TRL 4. Implementation was conducted in 2025 and the solution is being adopted within the FP1-MOTIONAL 2026 demonstration with representative data collected in the field during testing on a real rail infrastructure.
Maturity level
- Initial TRL: 3–4
- Current TRL: 5–6, corresponding to validation of the integrated data chain and analytics capabilities in relevant operational environments.
Expected benefits
- Reduction in maintenance costs (target up to 10%), through optimisation of maintenance intervals and reduction of unnecessary interventions.
- Reduction of in-service failures (target up to 25%), enabled by early detection of anomalies and improved reliability analysis.
- Improved asset availability and reliability, contributing to enhanced operational performance.
- Elimination of the need for hardware modifications, reducing deployment barriers, certification complexity and associated costs.
- High scalability potential, as the solution can be deployed across existing fleets without physical intervention.
Additionally, the solution reflects realistic deployment conditions by addressing heterogeneous rolling stock platforms and distributed data ownership.
Who benefits
Railway operators
Suppliers
Health Monitoring & Analytics of HVAC, Sanitary & Brakes Systems
The collected sensor data can be used to achieve the following benefits:
- Higher flexibility for maintenance and longer use of still-operational components, so that steps in the maintenance plan can be extended or even avoided.
- Detection of anomalies at a very early stage, to reduce the potential for in-service failures.
- Reduction of train maintenance workshop visits through combined repair actions.
- Combining different repairs makes it possible to reduce the frequency of trains in the workshop.
Maturity level
TRL 7
Expected benefits
Operational benefit: increase of rolling stock availability by avoiding failures during operation.
Economic benefits: replacement of components and maintenance only when necessary, based on the machine’s condition rather than a fixed schedule or specific mileage, resulting in savings on maintenance costs.
Who benefits
Railway Operators
Suppliers
Final users
Conclusion
Together, these solutions demonstrate how data-driven condition monitoring can support a more flexible and proactive approach to railway maintenance. By making better use of available data and enabling maintenance actions to be planned according to actual asset condition, they contribute to more efficient, reliable and scalable rolling stock operations.
Learn more about the project
FP3-IAM4RAIL
This solution has been developed within the Europe’s Rail (EU-Rail) Flagship Project FP3-IAM4RAIL. The project FP3-IAM4RAIL focuses on seven different integrated demonstrators for rail assets which are key for research and innovation in the rail sector.