A consortium led by Thales Ground Transportation Systems has secured a contract from Network Rail to develop and test fibre optic acoustic sensing (FOAS) technology.
This technology will help enhance safety and performance on the railway.
The data fusion of the FOAS technology will boost the ‘listening’ capabilities of optical fibres covering a length of 20,000km on Britain’s railways.
The technology is expected to improve the remote condition monitoring of assets and furnish data for minimising disruption for commuters and improving train performance.
Last November, Network Rail and Dutch rail infrastructure operator ProRail organised a design contest.
The contest challenged more than 40 suppliers to come up with suggestions for a funded 12 month outcome-focused trial of FOAS, IoT sensors, and smart CCTV cameras, amalgamated through intelligent data fusion and processing.
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By GlobalDataParticipants had to address four types of operational issues on the railway, including train movement and position reporting, rail and wheel defects, detecting trespass and people on the trackside, and level crossing safety management.
At present, Network Rail and ProRail are jointly working on research and development opportunities.
In March 2019, they signed a memorandum of understanding to work together, as well as share expertise to solve railway-related challenges.
The testing work with Thales’ successful bid will be carried out at Network Rail’s RIDC Melton test track, and on the mainline railway from Melton Mowbray to Leicester.
According to the companies, this trial work will begin this year.
Network Rail said in a statement: “The data could also be a valuable feed into the Rail Data Marketplace (RDM) recently announced by the government, providing a platform to share rail data across the industry and enabling a step towards a future data-driven railway.”
Last month, Thales, in partnership with Montreal-based technology start-up One Silicon Chip Photonics (OSCP), announced that it would develop a new technology for autonomous trains.
