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Costain develops system to predict rail faults

Signal work

A new system which predicts track and signaling faults and could make train delays a thing of the past is now being developed by University of the West of England (UWE Bristol) and Costain.

The system, which uses thousands of sensors and 3D modelling, will tap into big data and allow engineers to predict when part of a train track, signalling equipment or other devices at a station are likely to fail.

The university said augmented reality would then be used via a smartphone or a head mounted display to locate the failing components or structure faults. Engineers will then be able to access read on-screen instructions in real-time to help them with repairs.

The project is being led by engineering technology start-up Enable My Team, in conjunction with UWE Bristol and Costain.

The system is being tested in London Bridge Station where a network of internet of things (IoT) sensors will initially be installed this year. The sensors will gather data on tracks and station facilities, such as ventilation systems, barriers or lighting before sending it to a software called i-RAMP (IoT-enabled platform for rail assets monitoring and predictive maintenance) to analyse the data.

Artificial Intelligence will then be used to predict when a fault is likely to occur and highlight any stress points or component failures on a 3D virtual model of the station and tracks.

Completion of the trial is set for April 2020, after which the university said it will be trialled with selected customers for up to nine months.

Five other train stations in the UK have been approached to serve as testing sites for the technology with roll-out of the scheme planned for 2021.

UWE Bristol assistant vice-chancellor, digital innovation and enterprise Lukumon Oyedele said: “Every day in the UK, production is adversely affected by the hundreds of hours lost through train delays, often caused by faulty signal boxes or broken tracks.

“The system will enable companies to fix a problem before it even becomes one, and at a time when commuting is not disrupted, all thanks to the IoT sensors in the station and on the track.”

Oyedele said IoT sensors could transmit a whole variety of data including vibration, strain or pressure on a structure, humidity or temperature. Using several such components he said would enable train companies and station managers to monitor many parts of a train network at the same time.

Enable My Team founder & chief executive Sandeep Jain said: “i-RAMP could bring reliability to the 1.7bn annual passenger journeys on the UK railway, increasing productivity across the country.

“With machine learning and big data processing we can predict problematic vegetation, damaged structures and faulty signals, allowing repairs to be implemented before issues arise.” 

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