Regular, cost-effective collection of accurate track geometry and condition information by measurement of acceleration data from the train body and running-gear remains a key barrier to condition-based maintenance (CBM) approaches for rail-track maintenance. Adoption of CBM approaches could save \>£100M/yr in rail maintenance costs. Rail-track and vehicle maintenance is costly relying on scheduled and reactive maintenance often causing delays. In 19/20 Network Rail (NR) spent £1.395Bn/yr on track maintenance; and track fault speed-restrictions caused 2,460,453 delay minutes. NR use 7 inspection trains, each costing \>£8m to build and £5.5m/yr to operate, track is inspected every 4-26 weeks, insufficient frequency for CBM approaches. MoniRail's solution directly addresses the challenge and is a retrofittable, low-cost sensor systems will be fitted two Eurostar trains and data collected during an 8 month period from the trains running in-service and using data-analytics seek correlation and/or trends in the data to identify developing track and vehicle faults and identify underlying route causes for rough-rides. Development of predictive algorithms will provide engineers with early sight and prediction of failure to enable more effective planning of maintenance. The solution has the potential millions in maintenance costs and reduce delays for passengers.

The rail industry has seen reduced passengers and revenue and due to COVID-19, the challenge now is to attract passengers to a more reliable railway through diagnosing faults quickly and efficiently. HS1 has also had to deal with increased maintenance costs of network assets due to additional time using PPE and engineers traveling in separate vehicles to diagnose faults. The current data infrastructure isn't capable of reporting vast amounts of information in real time and doesn't allow engineers to quickly isolate the location and reason for the fault. The aim of the innovative R&D project is to implement remote condition monitoring to diagnose faults without need to wear PPE and travel to the equipment. 5G technology allows for real time data to feed into an augmented reality digital twin (ARDT), allowing for remote condition monitoring of rail assets and live data feeds to engineers at the point of need. The benefits include reduced costs, time and CO2 emissions as a result of maintenance activities taking less time to complete. The project team (led by PAULEY, supported by HS1, Athonet and University of Sheffield Advanced Manufacturing Research Centre (AMRC), will demonstrate the unique capabilities of a 5G enabled ARDT platform for use within a station, and several kilometres of track, as breakdown of lifts and escalators have a significant effect on passengers' journey and experience. The project will focus on the potential for ARDT to revolutionise the way in which rail engineers monitor and maintain equipment. The immersive environment will be modelled on St Pancras International. ARDT will support engineers in making effective decisions in a virtual environment. Broader impacts include: * Health and safety -- reduction in maintainers' travel and interaction with high voltage equipment, and reduced time spent on track. * Maintenance -- increase competency of engineers through the ability to visualise engineering work instructions which leads to quicker and more accurate repairs. * Skills and training -- provide opportunities for professional training and competencies that are currently learnt in situ. * Customer experience -- a more efficiently run and reliable rail network will ultimately lead to an improved experience for passengers and customers. * Scalability - Aligned to the National Digital Twin principles to enable integration into the wider rail network.