As part of the preparations and impact assessment for the next legislative framework governing European partnerships...
Public Consultation: Shape the future of European partnerships
read more
Smart Monitoring for Rail Anomaly Detection
Challenge
Keeping Europe’s railway infrastructure safe and reliable requires defects to be identified before they can affect operations. However, inspecting extensive rail networks through conventional methods can be time-consuming, costly and dependent on access to the track.
The solution
With the aim of making railway inspections more continuous and data-driven, Europe’s Rail (EU-Rail) brings together a solution that uses an eddy current sensor mounted on in-service trains and measurement trolleys to provide continuous, non-contact inspection of rail assets during normal railway operations.
Advanced signal processing extracts diagnostic features from high-frequency measurements to identify rail anomalies, insulation joints and fastening system defects.
Machine-learning algorithms analyse sensor data to automatically detect emerging defects, supporting predictive maintenance and reducing manual inspection effort.
Readiness for Industrialisation and Deployment
Status of the solution:
- Several full‑scale prototypes of the eddy current sensor have been manufactured and tested.
- Laboratory tests performed include:
- Creating anomalies i.e.: missing clamps and isolation joints, to train the machine-learning algorithms.
- Creating a processing pipeline for automated machine-learning training.
- Manufacture and fitting sensor brackets for the different trains.
- Field tests have been performed in several locations in Sweden and in Germany.
- Feature creation: once trained, the machine learning algorithms, once trained, can provide a parameterised software filter that can be loaded into the Lindometer to detect anomalies in real time.
- Ruggedisation tests: the field test sites in Sweden experience extremely cold weather in winter.
Current stage (2025–2026):
- Achieved TRL 7 for missing rail clamp detection.
- Manufactured and fitted sensor bracket to DB train.
- Performed a measurement campaign in Germany.
- Performed data analysis of field data for isolation joint deterioration monitoring.
Indicative timeline:
- 2026: completion of analysis of rail clamps; work on insulation joints is still ongoing.
- 2027+: continued development for the detection of rail cracks with tests being performed in Spain together with ADIF.
Expected benefits
- Earlier detection of rail and fastening system defects reduces the risk of infrastructure failures, improving network safety and operational reliability.
- Continuous monitoring from in-service vehicles lowers inspection costs, increases network coverage and reduces track access requirements.
- Supports the transition towards predictive maintenance, enabling infrastructure managers to optimise maintenance planning, extend asset life and reduce lifecycle costs.
The integrated IAMS demonstrator is ready for pilot‑phase deployment and further validation during 2026, with the goal of progressing toward full integration within Europe’s Rail operational frameworks.
Maturity level
TRL 6-7. The range is due to the TRL of the different features that the sensor can detect. TRL 7 for Missing Clamps and TRL 6 for Isolation joint deterioration.
Who benefits
Infrastructure Managers
Railway operators
Suppliers
Conclusion
Resulting from the work carried out through EU-Rail, this solution provides improved asset visibility through continuous condition monitoring, enabling earlier and more informed maintenance decisions.
By targeting interventions where they are needed most and minimising unnecessary track access, it can help lower inspection and maintenance costs while increasing network availability and reliability through the early detection of developing defects.
Learn more about the project
FP3-IAM4Rail
This solution has been developed within the 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.