WP9 Railway Infrastructure develops cutting-edge solutions for the design, monitoring, and maintenance of railway infrastructure. Using wayside, onboard, and crowd-based sensing combined with advanced analytics, WP9 delivers practical anomaly detection solutions, predictive models, and guidance for future standards. These insights feed into digital decision-support tools for maintenance. Showcased through European prototypes, one of our primary objectives is to transform railway data into actionable intelligence, enabling advanced maintenance strategies that result in fewer disruptions, lower costs, higher safety, and more sustainable operations. This, in turn, paves the way for a resilient, digital, and efficient European railway network.

WP13 Implements Intelligent Maintenance Tools for Civil Engineering Asset Management Railway Infrastructure.

Boasting more than ten demonstrators across five countries, WP13 promotes holistic asset management of civil infrastructures & surrounding areas by means of state-of-the-art technology. The teams of Italy, Spain, Norway, the Netherlands and France demonstrated monitoring technologies for bridges, slopes and tunnels. Utilising satellites, airplanes, ground and underground-based technologies, the team tackles problems related to subsidence, component fatigue, structural oscillations & failure and vegetation encroachment. WP13 consolidated the algorithm, and the measurement campaigns started with FP3-IAM4RAIL.

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.

The FP3 initiative aims to deliver integrated European solutions that reach Technology Readiness Level 6 (TRL 6), a stage where systems are demonstrated in real-world environments. Cluster C contributes to this goal by developing and validating technologies that are not only innovative but also ready for deployment.

This document outlines the development of safety requirements for the application of artificial intelligence (AI) in railway maintenance, focusing on ensuring compliance with European Union safety standards. Despite AI’s proven effectiveness, safety concerns remain a barrier to widespread adoption. As part of the ARGO project, DB Fernverkehr AG collaborates with experts in AI and railway safety to create structured safety argumentation using the goal structuring notation (GSN) method, based on standards such as DIN EN 17023 and Implementing Regulation (EU) No 402/2013. Each identified AI-related hazard is addressed through a dedicated safety case, with specific measures that must be fulfilled to enable operational deployment.

On November 12th 2024, Cluster B held a Workshop dedicated to showcase the availability of data coming from Work Package 3 (WP3) with the goal of supporting the development of Data Analytics and Machine Learning applications in WP4.

The meeting was held in Madrid, in the Hitachi GTS (formally Thales GTS) facilities and saw the participation of all Cluster B partners, both connected remotely and present physically.

The FP3-IAM4Rail team gathered for an important week of discussions and planning at the SNCF premises in Paris during the last week of November 2024. These meetings brought together first, members of the Technical Management Team (TMT) and in a later stage, the Steering Committee (SteCo) representatives to review progress, refine strategies and align efforts for the future of the project. We extend our heartfelt thanks to SNCF for their hospitality and for providing an exceptional venue that promoted productive exchanges and collaboration.

Within the scope of Work Package 3 (WP3) “Wayside Monitoring and TMS link: Design and Deployment” under ERJU Flagship Project 3 (FP3) IAM4RAIL programme, Hitachi Rail GTS Spain has participated in the implementation of Use Case 2, which aims to collect data from different assets and resources to achieve predictive maintenance for critical assets, as point machines, enabling informed decision-making related to traffic management activities.

The FP3-IAM4Rail project recently celebrated its Mid-Term Event, hosted at the UIC (International Union of Railways) premises in Paris. The gathering provided an opportunity for project partners, stakeholders, academia and industry experts to reflect on achievements, share insights and set the course for the second phase of the project.

InnoTrans 2024: A Retrospective on Innovation and Future Mobility Solutions

We are thrilled to share the latest developments from the FP3-IAM4Rail European project, a pioneering initiative within the Europe’s Rail Joint Undertaking. The first week of September, the Technical Management Team and Steering Committee gathered in Genoa, Italy, to engage in a series of decisive strategic discussions. These meetings represented a key moment in our ongoing effort to revolutionise railway asset management and drive the embracing of innovative technologies across Europe.

This document explores the main innovative technologies for the management and maintenance of railway infrastructure. The topics covered include the use of BIM for station supervision, track condition data fusion, the implementation of blockchain for virtual certification, automatic visual inspection by drones, and decision support systems for station asset management. These solutions aim to enhance the efficiency, safety, and reliability of the railway sector.

A newsletter about Cluster A has been published. Learn more about this Cluster that 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).