Experimental simulation of a bad contact in the power supply
Credit:
Reetz S, Neumann T, Schrijver G, van den Berg A, Buursma D. Expert system based fault diagnosis for railway point machines. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 2024;238(2):214-224. doi:10.1177/09544097231195656
At the final event the project team and guests discussed the role of Intelligent Asset Management in supporting the digital transformation, resilience and sustainability of rail infrastructure in Europe.
The focus of IAM4RAIL was on integrating condition data from advanced monitoring systems into decision-support tools and traffic management systems.
Through the use of artificial intelligence and digital twins, the project adopted a holistic, interoperable and cross-border approach at European level.
The final event took place on the 27-October 2026 in Madrid, Spain. It brought together representatives from European and national institutions, rail infrastructure managers, industry, research organisations and other key stakeholders. The event was a great opportunity to discuss the role of Intelligent Asset Management in supporting the digital transformation, resilience and sustainability of rail infrastructure in Europe. The programme included the presentation of project outcomes, demonstrations of developed tools and platforms, as well as roundtables on future perspectives under Europe’s Rail. Jörn Groos, project manager responsible for the DLR contribution to FP3-IAM4RIAL, joined the panel of the roundtable entitled “Predictive rail maintenance: How can non-invasive inspections improve rolling stock and infrastructure management” to discuss this topic at the heart of the project with experts from infrastructure managers and industry.
The FP3-IAM4RAIL project comprised seven integrated demonstrators for rail infrastructure. The focus was on integrating condition data from advanced monitoring systems into decision-support tools and traffic management systems. Through the use of artificial intelligence and digital twins, the project adopted a holistic, interoperable and cross-border approach at European level. In addition, technologies such as robotics and additive manufacturing were investigated to optimise asset management in the railway sector in a sustainable manner.
The DLR contributed to the project through research into embedded track-side and on-board condition monitoring systems, AI methods for holistic condition monitoring, digital twins and federated data spaces. See here for more details and recent publications. The entire DLR team is looking forward to progress in these research topics and to increase the technological readiness level of DLR solutions together with our project partners in the upcoming project FP3-IAM4RAIL-2.
Funded by the European Union. Views and opinion expressed are however those of the author only and do not necessarily reflect those of the European Union or Europe’s Rail Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them. The project FP3-IAM4Rail is supported by the Europe's Rail Joint Undertaking and its members.
