One Big Circle's approach to rail depot monitoring with DepotMATE

One Big Circle's approach to rail depot monitoring with DepotMATE

This innovative project aims to revolutionize the safety and efficiency of rolling stock inspections.

2024 marks a significant step forward for One Big Circle in improving depot-based train monitoring capabilities. The 12-month project aims to produce a portable system that can be customized for each partner operator, with the potential for wider commercial availability by the end of 2024. Following its successful bid in the First of a Kind (FOAK) competition, funded by the Department for Transport and delivered by Innovate UK KTN, the Bristol-based SME will progress its DepotMATE (Multi-sensor Automated Train Examination) project. This initiative is set to transform traditional methods of rolling stock inspection, providing a safer and more efficient solution.

DepotMATE aims to address the challenges of depot-based rolling stock inspections, which are often carried out in hazardous environments and require manual intervention. The project, in collaboration with Transport for London (TfL), Colas Rail, and East Midlands Railway (EMR), focuses on the development of a lightweight, multi-sensor inspection system. This innovative approach will facilitate remote inspections by capturing critical thermographic, acoustic, and video data from rolling stock, significantly reducing the need for manual trackside inspections.

The integration of DepotMATE with One Big Circle’s Automated Intelligent Video Review (AIVR) platform will enable depot operatives and fleet managers to access real-time insights and imagery, enhancing the safety and efficiency of maintenance operations. Mark Coney, Head of Fleet Engineering for EMR, emphasized the project’s potential to improve fleet services and its scalability for broader applications.

The AI models developed through DepotMATE will automate the detection of faults and problems, such as overheated wheel bearings, streamlining the inspection process. This technology is expected to support predictive maintenance decisions, contributing to the safe and efficient operation of rail vehicles.

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