Omnicom Balfour Beatty and the University of York have been working together for more than two years and have come up with a way to automate and digitalise railway line inspections.
It is estimated that the invention could save the rail industry £10m a year in track maintenance costs.
A camera is attached to the front of the train to inspect rail tracks. Using machine vision technology, high definition images of the rail track generate data that is then transferred through to a system for analysis of inaccuracies and faults on the tracks.
The technology not only identifies actual faults but also potential ones, allowing preventative fixes to be implemented as opposed to solely reactive repairs after an issue has arisen.
The automated technology is now being progressed from proof of concept into commercial grade software. Balfour Beatty hopes that it will provide a quicker, more efficient and safer alternative to the current manual track inspection process.
Balfour Beatty acquired rail technology specialist Omnicom Engineering in 2016.
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, said: “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.”
The knowledge transfer partnership project is funded by UK Research & Innovation through Innovate UK.
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