Thameslink train

Sensor enables trains to monitor condition of tracks

Image credit: Siemens

Rail vehicles will be able to gather data about the condition of tracks and rail bed thanks to a new sensor developed by researchers from the University of Huddersfield.

The technology, developed together with German engineering giant Siemens, can be easily fitted into each vehicle. Dubbed the Tracksure, the low-cost technology detects vibration from the tracks, which can pick up on under-track voids. The information is transmitted via a GSM-R cab radio system to a control centre.

“Initially we used simulation to identify what type of sensors and what accuracy and sensitivity would be needed for the Tracksure prototype,” said Farouk Balouchi, who introduced the technology at the recent Railway Condition Monitoring conference in Birmingham organised by the IET.

“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.”

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, crossings and bridges.

The researchers believe the technology would not only improve safety and reliability but also allow the network operators to reduce maintenance costs.

“Tracksure allows the network operator to make significant reductions in maintenance and delay costs, line closures, journey re-planning and speed restrictions, by having a reliable monitoring system that is non-intrusive yet gives analysis for the entire rail network, improving train safety, network reliability and passenger comfort,” said Balouchi.

The researchers want to expand the functionalities of the sensor. In future, they would like to be able to detect problems such as corrugation of the track or wheel flats - distortions in wheel shape caused by factors such as a lack of adhesion. Actual vehicle suspension faults could also be picked up by sensors.

Siemens wants to add information from the new sensor to its massive pool of of Big Data collected from its train fleets.

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