Track and Ride Condition Correlation for Optimised Rail Maintenance

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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.