Improving railway rolling-stock maintenance

The three-year project will see a team from the University’s Institute of Railway Research (IRR) and Centre for Planning, Autonomy and Representation of Knowledge work alongside a range of industry partners, including Northern Trains Ltd, Porterbrook and Unipart Rail.  It will investigate the use of industrial automation, advanced condition monitoring and data analytics, automated maintenance planning and scheduling, and the use of augmented reality.

The cost of maintaining trains constitutes a significant proportion of their lifecycle cost, so cost-efficient maintenance has clear advantages. Rolling-stock maintenance is traditionally carried out on a scheduled, interval-led basis and relies heavily on visual inspections. Advanced monitoring systems are already being introduced on new trains, and there is significant opportunity to exploit the data already being collected for early interventions and to develop a smarter approach to maintenance.

According to Dr Gareth Tucker, principal industry fellow at the IRR, the project will also help with the challenges posed by Covid-19. He commented: “Social distancing is hard for teams working under trains, so more automation is a way of reducing risk.” Regarding improving data analysis, Dr Tucker added: “Trains collect a lot of data about their condition, but we don’t think that is well processed. Condition-based maintenance works on things when needed, not pre-emptively to reduce the risk of it breaking in the future. [With better data analysis] there would be less unnecessary maintenance, which would help make trains more efficient and reliable.”