Imagine if trains and tracks could tell you exactly when they were about to break before it actually happened. This project builds a smart network of sensors and AI that acts like a health monitor for the entire rail system. It helps operators fix things only when needed and uses 3D printing and robots to make repairs faster and greener.
Rail operators face high lifecycle costs and service disruptions due to reactive maintenance and a lack of real-time data integration between asset health and traffic management.
A suite of 7 integrated demonstrators featuring AI-based decision tools, wayside and on-board monitoring sensors, and additive manufacturing processes for rail components.
If you are a network operator dealing with unexpected track failures and high maintenance costs — this project developed AI-based decision support and wayside monitoring that predicts anomalies to optimize maintenance strategies.
If you are a manufacturer dealing with inefficient production and short asset lifespans — this project developed additive manufacturing and innovative design principles that ensure sustainability and durability of rail assets.
If you are a freight operator dealing with unplanned downtime and routing delays — this project developed Condition-Based Maintenance algorithms and TMS connectivity that improve the reliability and availability of rolling stock.
It minimizes asset lifecycle costs by using predictive diagnostics and Condition-Based Maintenance to avoid unnecessary repairs and extend asset lifetime.
The project targets TRL 6/7 for integrated solutions, meaning it is designed for demonstration in relevant environments before full commercial rollout.
Based on available project data, specific licensing terms are not provided, but the project involves 98 partners developing interoperable European solutions.
It establishes a direct link between asset condition information and Train Management Systems (TMS) to optimize train routing decisions.
The project runs from December 2022 to November 2026, focusing on validation and future certification.
The project is heavily industry-driven, with 70 industrial partners representing 71% of the 98-member consortium. This high level of industrial participation, spanning 12 countries, suggests that the outputs are designed for immediate commercial utility rather than theoretical research, with a strong focus on cross-border interoperability.
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