Omnicom Balfour Beatty and University of York revolutionising railway inspection
A new Artificial Intelligence computer software, developed in partnership by Omnicom Balfour Beatty and The University of York, and supported by Innovate UK, is set to revolutionise the rail track inspection process and save the rail industry £10 million in track maintenance costs per year.
After two and a half years in a Knowledge Transfer Partnership – a collaborative relationship where Omnicom Balfour Beatty and The University of York share academic understanding and practical, industry-led insights – a state-of-the-art, machine-learning technology has been developed to digitalise and advance the way in which railway line inspections are carried out.
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.
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, 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.”
Ian Blakemore, Knowledge Transfer Adviser for the Knowledge Transfer Network which delivers the Knowledge Transfer Partnership Programme for Innovate UK, said: “This Knowledge Transfer Partnership has significantly improved the potential to automate rail inspection to an accuracy that will vastly improve the productivity of the whole checking process – something which is critical to the operation of the rail network. The partnership between Omnicom Balfour Beatty and the University of York flourished as a result of the discoveries and innovation that the close working delivered, adding real value to the business.”
Omnicom Balfour Beatty is committed to embedding practical solutions into projects with the help of technology and innovation. Working in partnership with those who understand the complexities of technology and combining expertise helps shape the industry and advance skills.
This partnership 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. This successful Knowledge Transfer Partnership project is funded by UK Research and Innovation through Innovate UK.
