IAM4RAIL

The goal of this project is to improve asset management and condition monitoring för CBM.

Project status and results

The FP3 – IAM4RAIL aims to provide innovative technical requirements, methods, solutions, and services – including technical requirements and standards for future developments – based on the latest cutting-edge technologies to minimise asset lifecycle costs and extend service life while meeting safety requirements and improving the reliability, availability, and capacity of the railroad system. Both infrastructure and rolling stock are addressed.

To achieve the operational outcomes targeted in this Flagship Area, several technical capabilities were identified by FP3. To develop flagship demonstrations, some functions must be delivered with enough maturity, with a target TRL indicated for each enabler.

• Enabler 1 - Scalable information platform to integrate and exchange information across the IAMS (Integrated Asset Management System) and TMS
• Enabler 2 -Asset diagnostic and inspection systems, including AI solutions and ML algorithms to analyse and combine information provided by different inspection systems
• Enabler 3 - Development of CBM methodologies and algorithms for freight applications
• Enabler 4 - New methodologies and technologies to leverage advanced and holistic asset decisions
• Enabler 5 - Digital Twins integrated with BIM to improve reliability, safety, efficiency, and effectiveness of asset management
• Enabler 6 - Advanced and holistic design and certification of assets
• Enabler 7 - Remotely controlled, unmanned and metadata-assisted interventions in construction, maintenance, and renewal operations, based on non-invasive or collaborative unmanned robotic actuators and wearables, or additive manufacturing techniques and validation standards for manufacturing and repairing assets

The Division of Operation and Maintenance Engineering at LTU is spearheading groundbreaking research across two key work packages within this project.

1. The first work package focuses on long-term asset management and lifecycle cost (LCC) analysis. Here, researchers at LTU are developing a sophisticated decision support system for availability simulations along track sections. This system leverages RAMS (Reliability, Availability, Maintainability, and Safety) parameters, accounting for the status of assets, components, and traffic, to optimize maintenance strategies. A significant achievement within this package is the creation of a planning tool designed to optimize tamping schedules.
2. The second work package centers on condition monitoring of track infrastructure. The division’s research in this area includes innovative magnetic field measurements of various track components, such as fasteners and insulation joints. Additionally, the division is also exploring distributed acoustic sensing using telecommunication optical fibers. This research aims to enhance the detection capabilities for anomalies not only in the track infrastructure but also in the vehicles moving on the track.

Through these efforts, the division is pushing the boundaries of infrastructure monitoring and maintenance optimization.