DMrail – Digitalisering av underhåll för en hållbar transportinfrastruktur
The need for the DMrail project stems from the increased demand on the rail network in terms of both the transportation volume, and the expected level of service flexibility needed for modern day life. DMrail looks at improving existing state-of-practice maintenance systems in order to increase the operational uptime of rail infrastructure. Hence, improving the social value and the environmental impact. Innovative methods for continuous health monitoring, data driven decision making, and efficient allocation of maintenance resources will be utilized to this aim. DMrail addresses the digitalization dimension of the InfraSweden 2030 and brings us one step closer to a connected transportation vision.
The expected impact of DMrail will be measured in terms of increasing the technology readiness level (TRL) of existing maintenance systems, adding value to the rail market, and improving the bottom line for rail operators. Specifically, the project aims at:
• Reaching a TRL of 6, where two prototypes for A.I.-based anomaly detection and remaining life prediction is demonstrated on Arlandabanan using installed sensor information.
• Raise the investment level in rail transportation by increasing it reliability and hence its attractiveness to people.
• Demonstrate a possibility of operating expenses reduction by 20% due to increased uptime.
The guiding vision of DMrail is “Easy to install, simple to use”. The innovation potential will be built around creating simple innovative measuring methods, and trouble-free installation and rational (dynamic) data transfer that is sufficient for an automated remote condition diagnostics and analysis that trigger maintenance activities as needed.
The DMrail project will implement core functionalities for intelligent data-driven predictive maintenance. DMrail core functionalities will be identified and developed according to the outcomes of the main project activities dealing with different repair supply chain actors and supporting different aspects in data connectivity/sensors, data analysis, predictive maintenance, and visualization.
Bombardier Transportation Sweden, Ekkono Solutions, Järnvägsklustret, RISE Research Institutes of Sweden, and KTH Royal Institute of Technology.