Automation News

Sensor System Could Turn Locomotives Into Track Monitors

13 Jan , 2017  

Railway experts at the University of Huddersfield Institute of Railway Research (IRR) are working with Siemens to develop an inexpensive and easily-fitted sensor that could turn virtually every rail vehicle into a track monitor, detecting and transmitting vital information about the condition of rails and rail bed throughout the network.

The result would not only be improvements in safety and reliability, but also major efficiency gains and cost savings for network operators, plus improved ride quality for passengers.

The track monitoring system, named Tracksure, has been developed in collaboration with the Institute of Railway Research, as part of the Remote Condition Monitoring Competition supported by the Rail Safety and Standards Board (RSSB) and Network Rail.

Every train in the UK – and a huge proportion overseas – is fitted with a GSM-R cab radio system. Siemens itself produces one of the most widely-used devices. It is now possible to retrofit an inexpensive Tracksure sensor card to the Siemens cab radios and by picking up vibrations they transmit information – received by a control centre – that can detect under-track voids.

These voids are gaps that have developed between sleepers and ballast. In serious cases, they can lead to an increased risk of rail breaks, along with poor vehicle ride performance. Tracksure would therefore provide early warning of problems – especially at switches and crossings and at the transition to other high value assets such as bridges.

The system has now been described at the recent Railway Condition Monitoring conference in Birmingham organised by the Institution of Engineering and Technology (IET).

“It was very challenging,” said Dr Farouk Balouchi from the Unversity of Huddersfield. “Initially we used simulation to identify what type of sensors and what accuracy and sensitivity would be needed for the Tracksure prototype. This led on to us developing a highly efficient algorithm which can process large quantities of acceleration data in a short space of time to detect the location and severity of potential track voids.”


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