Rail technology trial puts Shropshire company on fast track

Shropshire-based GOBOTiX has completed the successful trial of its automated rail inspection system, after it identified rolling stock faults in advance of them failing.

It is now seeking investment in order to take its prototype technology from a proof of concept to a mass market technology available to all rail operators, both in the UK and overseas.

Mark Schofield, director at Haworths Chartered Accountants, which has supported the company’s development, said: “GOBOTiX’s home-grown technology overcomes a significant pain point within the rail sector and is a fantastic example of a British business with high growth investment potential.”

During the 27-month project, GOBOTiX’s Vehicle Underside Examination System (VUES) successfully detected many different types of rolling stock defects and anomalies, including overheating components, damaged equipment, wearing gearboxes and cooling system failures.

Collectively, these detected failures would have saved the railways operator in excess of £250,000 in one year alone.

GOBOTiX founder Ben Davis said: “Continuous inspection and maintenance of rolling stock is a costly process for rail companies and even with regular checks, it can be difficult to detect faulty equipment and underlying defects. Our technology successfully detected radiator, cooling system and gearbox failures months before they led to any significant engine failures on the Chiltern Railways trains.

“VUES can deliver significant cost savings for train operators, not only by providing a greater lifespan of components, better utilisation of existing stock and increased fleet service life, but by reducing the level of fines that are awarded across the network for every minute a train is delayed.

The system was installed trackside and Chiltern Railways’ trains passed over VUES several times a day without any interruption to normal service routines. The technology successfully identified condition and health maintenance issues long before they were picked up during manual engineering inspections.

The equipment was part-funded by the Rail Safety and Standards Board and uses a combination of cameras, algorithms and non-visible wavelength light to detect anomalies and monitor rolling stock. The self-learning technology generates data and imagery, which is reported electronically to maintenance teams for investigation.

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