London Bridge Station receiving IoT upgrade to predict track and signal problems
Image credit: Dreamstime
A network of IoT sensors is to be installed at London Bridge station to gather a broad range of data to help prevent delays and train cancellations.
The new system, which has been developed by scientists at the University of the West of England, will collect data on tracks and station facilities, such as ventilation systems, barriers or lighting.
The information will be sent to a software programme called ‘i-Ramp’ (IoT-enabled Platform for Rail Assets Monitoring and Predictive Maintenance) which uses machine learning to anticipate potential faults before they happen.
The software will receive a variety of inputs such as high-resolution camera feeds from the front of trains, satellite imagery, weather forecasts and track-side sensors to make predictions about the impact on the train network’s infrastructure.
Information on when and where faults are likely to occur will be displayed on a 3D virtual model of the station and tracks.
The system will even be able to predict what is apparently one of the greatest threats to British rail: leaves falling on the line. It will also be able to tell when vegetation encroaches onto the railway or obstructs signalling and help to alleviate the worst effects of flooding and other natural disasters that could affect the smooth operation of the railway.
The system is set for completion in April 2020, after which it will be tested 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 and the roll-out of the scheme is planned for 2021.
The project is a collaboration involving the Bristol-based university, engineering firm Costain and technology start-up Enable My Team (EMT), which is the project lead.
Professor Lukumon Oyedele, who is the principal investigator on the project at UWE Bristol, 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.”
IoT sensors can transmit a whole variety of data including vibration, strain or pressure on a structure, humidity or temperature.
Using several such components will enable train companies and station managers to monitor many parts of a train network at the same time.
Sandeep Jain, founder of Enable My Team, said: “I-Ramp could bring reliability to the 1.7 billion 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.”
The system will also allow engineers to use augmented reality (AR) technology that offers them information about the location of faulty components and provide guidance on how to fix it.
As well as pointing them to the exact place where the problem lies, it will also supply them with real-time instructions and warn of dangers when carrying out the repairs
Prof Oyedele added: “By wearing a headset or using their mobile phones, engineers can view instructions superimposed on the joint or electrical circuit that they are repairing or replacing.
“For instance, it might give information or warnings about the presence of high voltage in a section of a control panel, or how to disassemble an electric circuit in a signal box in a safe way.”
In December, a six-month trial was launched to inform UK rail passengers about delays and cancellations through Facebook Messenger alerts.
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