Developed in partnership by Omnicom Balfour Beatty and The University of York, the AI computer software been developed to digitalise and advance the way in which railway line inspections are carried out. It is expected to save the rail industry £10 million in track maintenance costs per year.
The technology was created following two and a half years in a Knowledge Transfer Partnership, with support from Innovate UK.
Attached to the front of the train, a camera moves along rail tracks in need of inspection. The technology utilises machine vision, which captures high definition images of the rail track to generate data which is then transferred through to a system which analyses the data to highlight inaccuracies and faults on the tracks.
In addition, the technology assists in identifying where faults may occur, allowing preventative fixes to be implemented as opposed to urgent repairs after an issue arises.
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The automated technology, which is currently being progressed from proof of concept into a commercial grade software, is set to provide a quicker, more efficient and safer alternative to what is currently a manual track inspection process.
By automating the inspection process, the health and safety of workers will improve by minimising their exposure to live track environments as well as reducing time taken to complete a manual inspection.
Stephen Tait, Head of Operations for Omnicom Balfour Beatty and Project Lead, said: "We are developing digital technologies that are rapidly changing our industry; from 'predict and prevent' technology and advanced digital surveying techniques through to data science. All of our solutions are underpinned by a long legacy of design and construction expertise.
"Our collaboration with the University of York has been invaluable; this latest innovation is an excellent example of how Balfour Beatty continues to deliver our commitment to reduce our onsite work by 25% by 2025 as we progress against our commitment to develop technologies to evolve the digital railway for a more reliable, cost efficient and safe network for all users."
Professor Richard Wilson, lead researcher on the project from the Department of Computer Science at the University of York, added: "These machine vision technologies for high speed rail inspection will improve the reliability of the railway network, reduce costs and increase the safety of manual inspection. The computer vision and machine learning technologies provide automated inspection of complex assets such as junctions and crossings."
Omnicom Balfour Beatty received financial support from the Knowledge Transfer Partnerships programme. Knowledge Transfer Partnerships aim to help businesses to improve their competitiveness and productivity through the better use of knowledge, technology and skills that reside within the UK knowledge base.
(CM/JG)
