Maestrano’s market-leading LiDAR & AI technology to provide analytics and alerts on the UK’s fastest railway to cut costs and improve safety.
Maestrano Group PLC (AIM: MNO), the artificial intelligence platform for transport corridor analytics, is pleased to announce a new contract with Network Rail High Speed, “NR(HS)”. The five-month Proof of Concept (POC) contract has been awarded to Maestrano’s specialist rail analytics subsidiary, Cordel.
NR(HS) delivers the maintenance of engineering infrastructure assets for the High Speed One (HS1) railway. The POC will determine the suitability of Cordel’s technology for monitoring the overhead line, vegetation, ballast profiles, track and passing clearances. HS1 is the UK’s high-speed railway running 109km from St Pancras International in London to the Channel Tunnel, and connecting the UK with international high-speed routes.
As part of the POC contract, Cordel hardware, including the Cordel LiDAR scanner and machine vision cameras, will be mounted on a multi-purpose rail vehicle (MPV). Typically, Cordel’s automated inspection will patrol the line every two weeks, capturing a complete picture of the network. The point cloud and video data captured will be uploaded into Cordel’s Deep Machine Learning platform, which will analyse the results and automatically create insights for NR(HS) engineers. Cordel’s software has an easy-to-use interface providing railways with survey-grade accuracy, high-resolution video, and historical data comparisons.
The system will detect out-of-course movements in the overhead wiring above the trains or the ballast beneath them, along with any excessive vegetation growth into the rail corridor. Cordel’s software will also accurately locate any issues, so they can be targeted for remedial attention. In a first deployment for the UK, Cordel technology will conduct automated height and stagger measurements on the HS1 overhead line to check that all is well. Cordel’s system will complete the required inspection in a fraction of the time, providing efficiency benefits.
The new contract is Maestrano’s second, via Cordel, with the UK rail network. In December 2020, the company announced that Cordel had been awarded a 12-month contract with Network Rail after a fully funded six-month trial. This agreement brings established gauging automation to Network Rail to measure the location and encroachment of vegetation and to check track clearances for trains to pass. As well as in the UK, Cordel has contracts with major railroad companies around the world, including ARTC in Australia and Meitetsu in Japan. In June 2021, the Company announced its first paid project in the US with Union Pacific Railroad.
Nick Smith, CEO, Maestrano, said: “By conducting faster, more accurate, and more frequent inspections, railways like HS1 can save money, deliver travellers a better service, and improve safety. Cordel’s machine learning technology alerts engineers promptly, enabling emerging and potential issues to be addressed cost-effectively. The NRHS contract is the first time our hardware has been deployed in tandem with our software in the UK, and we are confident that once this trial is complete, it will be adopted on a longer-term basis. As HS1 is designed and built to the same specification as all High-Speed European routes, validating our technology through this contract will enable us to engage with the whole Trans European Network.”
Ian Buddery, Chair of Maestrano, commented: “This new contract proves yet again the value and effectiveness of our technology within the UK Rail industry. The data we collect is enabling train companies to be much more efficient, transforming the way they work. Each new project advances our Machine Learning algorithms, extends our worldwide competitive advantage, and highlights the growth opportunity we have.”
The information contained within this announcement is deemed to constitute inside information as stipulated under the Market Abuse Regulations (EU) No. 596/2014, which is part of UK law by virtue of the European Union (Withdrawal) Act 2018. Upon the publication of this announcement, this inside information is now considered to be in the public domain.
