FP3-IAM4Rail project: a journey through Europe’s railway future

Explore the future of European railways with the FP3-IAM4Rail Express project! From AI-powered predictive maintenance to additive manufacturing repairs, smart sensors and augmented reality tools, this EU-Rail project demonstrates how innovation is reshaping European rail for safety, efficiency and sustainability.

Clusters

Demonstrators

Discover the 6 Clusters of the FP3-IAM4Rail Project by ERJU

Welcome to the video series showcasing the 6 Clusters of the FP3-IAM4Rail project, part of the ambitious European Rail Joint Undertaking (ERJU) initiative.
The FP3-IAM4Rail project focuses on transforming the future of European railways by promoting innovation, sustainability and cutting-edge technologies. To achieve this, the project is structured into six strategic Clusters, each targeting a specific area of improvement to make the rail system more efficient, sustainable and customer oriented.
Through these videos, you’ll gain insight into the unique objectives, challenges and solutions proposed by each Cluster. Whether you are a stakeholder, researcher or simply passionate about the future of rail transport, these presentations will help you better understand how FP3-IAM4Rail is contributing to building a smarter and greener railway system.

Explore the videos below to learn more about the work being carried out within the six Clusters:

• Cluster A: Transversal Activities. This cluster unifies the common activities of the project including project coordination (Work Package 1); system vision, architecture and validation (Work Package 2); and dissemination, communication and exploitation activities (Work Package 20).

• Cluster B: Wayside Monitoring and Traffic Management System Link. The cluster focuses on the design, development, testing and validation of an Intelligent Asset Monitoring System capable of supporting the railway operators and infrastructure managers in maintaining smooth and uninterrupted operations (Work Package 3 and Work Package 4).

• Cluster C: Rolling Stock Asset Management: On-board and Wayside Technologies. This cluster addresses both on-board (Work Package 5 and Work Package 6) and wayside (Work Package 7) monitoring technologies for the design, testing and validation of intelligent rolling stock asset management solutions.

• Cluster D: Infrastructure Asset Management. The infrastructure asset management cluster addresses (i) long term maintenance and costs (Work Package 8); (ii) track systems (Work Package 9); (iii) innovative multi-purpose IAMS infrastructure applications (Work Package 10 and Work Package 11); and (iv) civil assets including structures, earthworks and geotechnics (Work Package 12 and Work Package 13).

• Cluster E: Railway Digital Twins. This group of developments focuses on the implementation of railway Digital Twins across the rail sector (Work Package 14 and Work Package 15).

• Cluster F: Environment, User and Worker Friendly Railway Assets. Cluster F has the objective of creating environment, user and worker friendly railway assets addressing environmental and cost-effective lines (Work Package 16), new additive manufacturing repair processes (Work Package 17), robotic platforms for railway interventions (Work Package 18) and Augmented Reality and exoskeletons to support railway maintenance (Work Package 19).

Demonstrators

As part of the FP3-IAM4Rail project, we are pleased to present a video demonstration of the Intelligent Asset Management System (IAMS), a key innovation designed to support data-driven decision-making in railway operations and maintenance.

 

Demonstrator LDV in The Netherlands

Our FP3-IAM4Rail project has conducted successful Laser Doppler Vibrometer (LDV) tests in the V-track as well as onboard the train of TUDelft in the Netherlands. This high-precision, non-contact vibration measurement technology helps to monitor dynamic behaviour directly on moving trains. The LDV technology testing is a crucial milestone towards predictive maintenance and intelligent rail infrastructure. We can now measure input-output responses from railway track components!

Trakside Acoustic Monitoring

Sounds from passing trains carry not only the rhythm from the Doppler effect but also valuable information on bogie health condition. The Alstom Bogie Mechatronics team has developed a trackside acoustic solution for monitoring bogie outboard components. In the frame of Europe’s Rail FP3-IAM4Rail project, an extension to the trackside acoustic solution is under development, with the goal of monitoring bogie inboard components. Prototype tests will be performed on the networks of project partners.

 

Prescriptive maintenance – Approach from measurement to maintenance shift planning

Turning away from reactive maintenance triggered by defects, DB is striking a new path toward prescriptive maintenance. Track maintenance work is derived from predictions of track geometry and is scheduled in a timely, bundled way, increasing both efficiency and planning reliability. Supported by monitoring technology and new eddy current sensors, root cause analysis enables a far more sustainable approach to maintenance. The shift planning tool developed in FP3-IAM4RAIL incorporates the contemporary DB Maintenance Container and interfaces with the shift coordination tool currently in service, aiming for data-driven and hands-on testing within DB’s real maintenance planning processes.

 

Robotics in Railway Maintenance: Trenitalia’s Contribution to the EU-Rail FP3-IAM4Rail Project

The video compellingly highlights two groundbreaking initiatives in which Trenitalia plays a crucial role within Work Package 18 “Robotics Platforms”, of the European FP3-IAM4RAIL project, part of the Europe’s Rail Joint Undertaking (ERJU). The primary aim is to integrate advanced robotics into key railway maintenance processes, thereby significantly boosting efficiency, enhancing safety and promoting sustainability. The first initiative features the Disinfection Robot (DR), developed entirely within the project in collaboration with SNCF and PKP. This robot is designed to be integrated into sanitation activities and, in the subsequent phase (wave 2), into the cleaning of rolling stock. It advances through scenarios of increasing Technology Readiness Levels (TRLs), effectively demonstrating the practical value of collaborative robotics in supporting daily maintenance operations. Adding to this is ARGO, a robot patented by Trenitalia in an Open Innovation initiative with the Scuola Superiore Sant’Anna and developed outside of the FP3-IAM4RAIL project. Its integration into the European project aims to enhance and amplify its significant contributions related to neural network training for cooperation scenarios, utilising AI and computer vision techniques for undercarriage inspections. Together, the Disinfection Robot and ARGO exemplify how technological innovation and European collaboration are laying the groundwork for a more digital, safe and efficient railway maintenance system. This advancement ultimately benefits both operators and passengers, setting a new standard for the industry.

 

Augmented Reality:

This video, called “Augmented Reality Tools to help and guide railway workers in maintenance operations” and representing UC19.2, presents a new support tool for infrastructure maintenance operations based on mixed reality. The system starts with an authoring tool where system designers can define a maintenance task in a simple and visual way without the need of programming. The information of the task is then transferred to a mixed reality application that guides the operator. The application shows a 3D interface seamlessly integrated into the workspace, where the instructions are easy to follow. The video demonstrates this application during the set-up of a track measuring device.

FP3-IAM4RAIL – VIDAR: Autonomous Robot Enabling Smart, Data-Driven Railway Maintenance

This video presents VIDAR – Versatile Inspection, Diagnostic and Autonomous Repair robot, an on-rail robot for autonomous railway inspection and diagnostics. Developed for Trafikverket (Swedish Transport Administration) by Chalmers University of Technology in cooperation with SNCF for the EU project FP3-IAM4RAIL, within Europe’s Rail Joint Undertaking, the robot is designed to perform regular, unsupervised inspections and, in the long term, minor repairs.

The demonstrations were carried out at Trafikverket’s Tortuna Test Center (TTC) in Västerås, Sweden, in 2025. In the first test, the robot navigated autonomously to predefined inspection zones, carried out inspections, and returned to its base. In the second demonstration, VIDAR autonomously detected, localised and documented rigged rail defects representing track irregularities. Using RTK-GNSS for precise positioning and geo-fencing, the robot successfully identified all faults, triggered video recording at each defect location and returned safely to base.

The tests were repeated successfully and confirm the platform’s ability to perform reliable, repeatable inspection rounds with no human intervention. The video illustrates the achievement of TRL 6 for autonomous inspection functionality and demonstrates a key step toward fully autonomous railway maintenance.

 

 

DEMONSTRATOR OBJECTIVES:

Demonstrator Objective 1 (DO1):

Integration between the Intelligent Asset Management System (IAMS) and the Traffic Management System (TMS)  of the FP3–IAM4RAIL Project showcases the integration between the Intelligent Asset Management System (IAMS) and the Traffic Management System (TMS) across railway assets.

The objective of DO1 is to demonstrate how secure and standardised interfaces, methods and processes can enable efficient data exchange between IAMS and TMS, supporting smarter and more coordinated railway operations.

The demonstrator includes:

• Advanced data analytics for predicting and prescribing maintenance actions for both wayside and rolling stock assets, based on heterogeneous data sources.
• Integration of analytics results with TMS and Operations & Maintenance (O&M) tools to optimise traffic regulation, train routing and maintenance activities.
• A system-level approach delivering interoperable and integrated European railway solutions at TRL6 maturity.

Within FP3–IAM4RAIL project, Demonstration Objectives validate how intelligent asset management and traffic management can work together to improve reliability, operational efficiency and decision-making across the railway network.

 

Demonstrator Objective 2 (DO2):

Asset Management & Rolling Stock, developing new monitoring and inspection systems leading to decisions and planning of interventions amongst various use cases:

As part of the FP3–IAM4RAIL project, Cluster C plays a key role in transforming the way rolling stock maintenance is managed across Europe. It is focused on developing intelligent technologies that enable real-time monitoring of train conditions, both onboard and from strategically placed trackside systems. These innovations are showcased under DO2, which highlights how monitoring technologies can support data-driven decision-making and smarter maintenance planning resulting in a reduction of maintenance costs and in service failures.

In this video, we present an overview of several use cases being addressed within Cluster C, including UC5.3, UC6.8, UC7.1, and UC7.5.

Demonstrator Objective 3 (DO3):

Long Term Asset Management, developing decision support applications for asset management and Life Cycle Cost (LCC) optimisation, amongst various use cases:

This video shows a Decision Support Tool for dynamic maintenance planning, based on holistic asset condition analysis from multiple data sources. It enables improved decision-making through integrated diagnostics, maintenance record correlation and data analytics that provide insights across different sources, supporting root cause analysis and failure prediction/forecasting, as well as dynamic maintenance planning based on real-time and historical asset conditions.

The solution enhances reliability by preventing failures, reduces downtime through targeted maintenance, and increases resource efficiency by aligning workforce allocation with the actual needs of the assets. Additionally, it enables adaptive planning based on data that evolves according to asset behaviour and environmental conditions.

Demonstrator Objective 5 (DO5):

Asset Management & Digital Twins to support the design, maintenance, upgrade, and renewal of railway assets, amongst various use cases:

The video presents the Work Package 15 Integrated Demonstrator developed within the FP3-IAM4RAIL project, showcasing how Digital Twins, BIM, AI and blockchain technologies can be combined to support railway asset management and virtual certification. The demonstrator integrates infrastructure digitalisation, diagnostic data fusion, point machine simulation and blockchain-based certification into a single digital workflow. It highlights how advanced digital technologies can improve asset monitoring, traceability and decision-making, paving the way for more efficient and trustworthy railway infrastructure management.

 

Demonstrator Objective 6 (DO6):

Design & Manufacturing, showcasing the eco-friendly design, production and reparation of resilient assets including Additive Manufacturing (AM), amongst various use cases:

This video presents Demonstrator Objective 6 (DO6) of the Europe’s Rail FP3-IAM4RAIL project, showcasing innovative solutions for the sustainable design, monitoring, production and repair of railway assets. By integrating the results of Work Packages 16 and 17, it highlights advances in eco-design, predictive maintenance, resilient infrastructure and additive manufacturing technologies, contributing to a more sustainable, efficient and reliable railway system.

 

Intelligent Turnout: this video showcases a solution from voestalpine Signalling Austria GmbH for monitoring the condition of turnouts through the installation of sensors, which also enables better planning of maintenance activities. This use case is being implemented in close collaboration with ADIF and many other organisations within the Spanish rail network. This video highlights the possibilities offered by turnout monitoring in the future. The smart points fully meet the objectives of the FP3-IAM4RAIL project in terms of enhancing safety through digitalisation and reducing downtime thanks to better planning of maintenance operations.

 

Eco-friendly turnout: this video shows the stages from development, production and assembly through to the installation of the eco-friendly turnout on the Austrian Federal Railways network in Carinthia. This use case focuses on optimising the load distribution of trains passing through the turnout and reducing CO₂ emissions during production and throughout its service life. The ‘Green Turnout’ fully meets the objectives of the FP3-IAM4RAIL project in terms of reducing CO₂ emissions and extending the turnout’s service life.

 

Development of additive manufacturing processes for railway crossings: this video shows the steps involved in repairing a railway crossing under laboratory conditions using WAAM technology. The welding is carried out by a robot and, at the end, the weld bead can be seen.  The use case ‘Additive manufacturing process development for crossings’ fully meets the objectives of the FP3-IAM4RAIL project in terms of reducing production costs and reusing materials.

 

Repair welding of railway crossings: this video shows the 3D scanning of a section of a railway crossing, followed by its analysis and fault detection. This is followed by a simulation of the welding process, the creation of the welding path and, finally, the welding itself.  The ‘Repair welding for crossings’ use case fully meets the objectives of the FP3-IAM4RAIL project in terms of reducing the time required for the operational process and cutting costs.

 

Demonstrator Objective 7 (DO7):

Robotics & Interventions showcases high-tech automated solutions for construction and execution of interventions supported by robotics and wearables, amongst various use cases:

This video, called “Upper-body exoskeleton for worker’s support in railway industry” and representing UC19.1, presents a new upper body exoskeleton designed to provide support to railway workers during overhead tasks. Musculoskeletal disorders are one of the leading causes for sick leave in railways infrastructure maintenance workers as these tasks, in many cases, require lifting heavy weights and maintaining unergonomic positions. FP3-IAM4RAIL addresses this issue by developing a novel upper-body active exoskeleton. This exoskeleton will support the operator by offloading weights using its actuators. Preliminary tests were run at Bologna San Donato Railway test circuit. There, the ergonomics of the device and its actuation were tested.

 

PRIME, autonomous and modular railway maintenance robot: this video presents PRIME, an autonomous infrastructure maintenance robot developed under the European FP3-IAM4RAIL project to monitor railway tracks. Equipped with laser sensors, it autonomously measures track gauge to detect safety anomalies and prevent derailments. The prototype can adjust its speed, move backward to re-analyse uncertain data and automatically stop when encountering obstacles. While it operates independently for most of its journey, it safely stops before level crossings to wait for human operator validation. Designed to reduce maintenance costs, this modular platform aims to reach industrial production within five years.

 

Robotic Tightening Torque Test for AV1 Sleeper Screws: this video demonstrates an automated tightening torque test performed on AV1 sleeper screws, developed within the framework of the FP3-IAM4RAIL project. The process integrates advanced robotics, computer vision and force feedback to ensure precise and reliable testing. A computer vision system accurately identifies and locates the AV1 sleeper screws. The robotic system aligns and couples the screwdriving tool to the screw. This delicate coupling process is dynamically guided by real-time signals from a force sensor. During the tightening process, the same force sensor continuously measures the applied torque. The system is programmed to automatically stop the test once the target tightening torque of 220 ± 20 Nm is successfully reached.

 

ARGO: ARGO (Autonomous Robotic inspection of rollinG stOck), is an intelligent robot developed to automate inspection activities and support predictive train maintenance. Thanks to the use of advanced sensors, artificial intelligence and innovative robotic systems, ARGO enables rapid, standardised inspections, thereby helping to improve the safety, efficiency and sustainability of railway maintenance processes.

 

Disinfection Robot (DR): DR  is an autonomous robotic solution developed within the European FP3-IAM4Rail project to support railway sanitisation and disinfection processes. Using advanced navigation systems, AI-based people detection and modular robotic technologies, the robot aims to autonomously operate inside railway vehicles, improving the effectiveness, safety and efficiency of disinfection activities, while reducing operational downtime and biological risks for operators.