August 27, 2026

Holistic Condition Monitoring for Predictive Maintenance

Visit us at Booth 440, Hall 2.2 at the InnoTrans 2026!

We look forward to discuss the possibilities and potential of predictive maintenance for your infrastructure!

Ensuring the reliability and safety of railway infrastructure at manageable costs requires a shift from corrective or simple preventive to predictive maintenance. Our research group focuses on the holistic condition monitoring of critical assets such as switches and rails as well as the track superstructure as a whole. By fusing high-resolution data from embedded wayside sensors, vehicle-borne monitoring, and existing maintenance records together with environmental data, we develop AI-driven algorithms that continuously provide actionable insights into asset health.

Our approaches are field-proven through long-term deployments in operational railway environments at the main line, industrial networks, and urban light rail networks (TRL 5-6). We offer modular, licensable software implementations designed for easy integration into R&D environments of infrastructure operators and technology providers. Explore our current projects and latest publications below to see how we are shaping the future of sustainable rail infrastructure.

Projects

IAM4RAIL - Intelligent & Integrated Asset Management - Europe's Rail (Flagship Project 3)

OnboardEU – automatically detect damage to the track with AI

Streetcars are highly frequented means of public transportation that also run through densely populated city districts. Passengers and residents usually find the noise and vibrations caused by damage to the tracks particularly annoying. The damage also means higher costs for the vehicle operators. This is where the OnboardEU project comes in, in which the German Aerospace Center (DLR), together with the AIT Austrian Institute of Technology GmbH, Vienna, and i4M technologies GmbH, Aachen, is developing solutions for the automatic detection of damage using AI.

Digital switch 2 – Autonomous and continuous condition monitoring

The digitalisation and automation of condition monitoring and forecasting in the rail infrastructure sector is one of the decisive factors in maintaining and expanding the performance of the rail network in the long term while reducing maintenance and repair costs.

IntelPlaMo

As part of the IntelPlaMo project (Intelligent condition analysis for mission planning and monitoring of safety-critical infrastructure), the German Aerospace Center (DLR) is developing innovative methods for the automated surveillance of critical infrastructure. This involves combining data from ground-based sensors with data from mobile autonomous systems, particularly Unmanned Aerial Vehicles (UAVs). This multimodal sensor data is then fed into a back-end data ecosystem where it is processed using AI-based algorithms and visualised in a digital control centre.

SMARAGD

IAM4RAIL - Intelligent & Integrated Asset Management - Europe's Rail (Flagship Project 3)

Schwelle 3.0 – Use of AI methods for DB-InfraGo inspection tasks

In the Schwelle 3.0 project, the DLR Institute of Transportation Systems is investigating the suitability of artificial neural networks for detecting cracks in concrete sleepers on behalf of Deutsche Bahn.

HavenZuG – Efficiently prevent damage to the track

Harbour railways ensure the transport of goods in German seaports and inland ports. In order to be fully operational at all times, their tracks must be reliably and cost-effectively protected against damage. The solution for this is continuous condition monitoring and preventive maintenance based on this. The aim of the HavenZuG project is the prototypical implementation, testing and further development of embedded condition monitoring for harbour railways in operational shunting operations.