The demand for rail services is rising rapidly and with higher volumes of passenger traffic come increased maintenance requirements. The current cost of maintaining the UK’s rail infrastructure is over £1b per annum, accounting for 18% of Network Rail’s expenditure. Moreover, all maintenance operations, whether planned or unplanned, cause significant disruption to rail services. Therefore, optimised railway infrastructure maintenance is key to achieving reliability, availability and safety of rail transport systems.
A risk-based inspection (RBI) methodology can improve the overall safety of critical plant whilst reducing the duration and cost of inspection and maintenance. This is achieved by evaluating ‘all risk’ as a product of the ‘likelihood of failure’ and the ‘consequence of failure’. Network Rail has recognised that a predictive model for automated risk analysis of railway tracks could deliver a reduction in the number of service failures, and subsequent downtime, in turn leading to fewer unplanned maintenance operations and reduced disruption to services.
The OptRail project is addressing the rail industry’s need for improved service levels by developing an automated system for the optimisation of railway track maintenance programmes. Optical fibre sensors will provide continuous real-time monitoring of the track condition. Risk-based models will allow areas of high risk to be identified before a defect is detected through artificial intelligence models.
The OptRail consortium, comprising RCM2 Ltd, TWI, Brunel University London, Yeltech Ltd and Surrey Advanced Control Ltd, recently met for their fourth quarterly meeting at TWI, Cambridge. The consortium shared updates on the simulation of the mechanical strain in the centrally loaded rail, and the selection and installation of Fiber Bragg Grating (FBG) sensors. The sensitivity of the sensors was evaluated in a lab test using a four-point bending test rig to perform static and dynamic loading on an intact track test piece.
The next steps in the project will be signal processing and failure data analysis, and further experiments with increasing defect size are planned to evaluate the sensitivity of sensors from other locations.
The OptRail project started in April 2018 and has received funding from Innovate UK under Ref. No. 104246.