Skip to content

150 million to build excellence in maintenance research of railway system

Published: 27 May 2021

Trafikverket is investing 150 million over ten years in pioneering railway research at Luleå University of Technology. Luleå Railway Research Center (JVTC) is trusted to build excellence in research of railways, with a focus on operation and maintenance of infrastructure and railway bridges. This grant from Trafikverket will facilitate the Swedish railway sectors efforts to achieve increased operational reliability and extended service life, higher capacity and safety which is expected to result in higher punctuality of trains.

– Trafikverket's investment is a proof that Luleå University of Technology is at the forefront of its research and innovation in railway maintenance in Sweden. For the Swedish railway sector, this investment in building knowledge and facilitating innovation will contribute towards attractive and sustainable railway transport. The impact of this investment will be realised in form of reduced operational disruptions, unplanned stops, derailments and that the trains will get more punctual, Uday Kumar says , Professor of Operation and Maintenance at Luleå University of Technology and Director of JVTC.

Planning for educating engineers for a MSc degree

The investment takes place within the framework of the Trafikverket's long-term research funding, which is divided into a number of areas of excellence for the Swedish railway secto,  where the area of Operation and Maintenance Engineering is led by Luleå Railway Research Center (JVTC) at Luleå University of Technology. It is also about knowledge building and knowledge dissemination in the form of state of the art Research and Innovation projects and hireing more undergraduates. Luleå University of Technology plans to start a MSc degree Program for engineers with a focus on sustainable transport and mobility.

Demand-based railway research

– We are very proud that JVTC has been commissioned to be responsible for excellence-building research in the area of Operation and Maintenance of railways in Sweden. Our focus on applied and needs-based research at Luleå University of Technology conducted in close collaboration with the railway industry has yielded results, Birgitta Bergvall-Kåreborn says, Vice-Chancellor at Luleå University of Technology.

The railway assets decides the maintenance and repair time and type of actions themselves with the help of AI

Research in Operation and Maintenance Engineering at Luleå University of Technology focuses on increasing the capacity and improving the operational reliability of our railway systems. This is done through streamlining the operation and maintenance processes through maintenance strategies with predetermined, condition-based, predictive and remedial maintenance, as well as data-driven decision support with the help of industrial Artificial Intelligence (AI), where the railway system can automatically predict the need for maintenance.

– We are developing an AI platform where the health information of different railway assets can be integrated with context information such as business scenarios, maintenance support status, location information, climate information and climate change models, etc., to identify the best possible maintenance tasks and times to execute the identified actions. More and more people travel by train and the volume of postponed required maintenance actions demands new business and engineering solutions to meet the challenges and in this respect this initiative by Trafikverket, is timely and very important, Uday Kumar says.

Facts about research within JVTC: Within JVTC, two projects have been selected for The Royal Swedish Science Academy (IVA)'s 100 list in 2020 and 2021. The ePilot and AI Factory projects have, through broad industry collaboration, created an innovative digital platform that materialise, test and apply research results to railway operations. The projects accelerate digitalisation and create benefit to the railway industry by facilitating fact-based, data-driven decisions. The DigiSwitch project has developed a new method that enables automatic condition monitoring and analysis of wear in switches, damage to train wheels, track alignment faults and the risk of accidents due to so-called run-up switches. Read here for more examples of other research projects.