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.