Sensors measure lateral position of rail bogie wheels relative to the track25 March 2020

ActiWheel is an autonomous control system that uses AI to guide the carriages along the tracks. This is said to enable faster, smoother and more economical rail travel.

Laser triangulation sensors from Micro-Epsilon are being used to measure the lateral position of rail bogie wheels relative to the rail head in a rail traction technology project by Derby-based company SET Limited.

ActiWheel is a traction system that uses artificial intelligence to guide trains along the tracks, said to enable quicker, smoother and more economical rail travel.

Martin Whitley, director at SET Limited, explains: “This revolutionary wheel motor can be controlled to produce more driving force on one side or the other in order to steer the wheelset down the centreline of the track. For the last 200 years, conventional rail vehicles have only had a solid axle and wheel coning providing this capability which drives some significant compromises and issues. What we want to do is to see this technology implemented as the technology of choice for rail passenger vehicles of the future.”

In the ActiWheel solution, the motor is integrated in the wheel, which means there is no transmission between the two and no moving parts beyond a bearing which every wheel and axle has. In addition, there is no friction braking systems, reducing the amount of maintenance required.

Neil Cooney, technical director at SET Limited, comments: “One of the biggest issues facing the rail infrastructure is Rolling Contact Fatigue [RCF], which occurs due to the energy in the contact between the wheel and the rail. Conditions under the contact patch are always severe and the yield stress of the rail wheel is always exceeded, on at least a microscale.

“Actively yawing the wheels along with active torque control, manages the contact patch at the optimum point of the rail, practically eliminating RCF. This is because ideal [close to radial] steering reduces the energy in the contact patch to a point where RCF does not occur and wear is very small.”

For the last nine months, ActiWheel has been put through its paces in a technical demonstrator project. An ex-London Underground train with a single carriage was fitted with an ActiWheel system on each of its eight wheels. The project was a success, says Cooney, with the results proving that wear and RCF were almost negligible. The project has already received some industry interest and SET is engaged in developing a system to a network rail.

For this project, each ActiWheel system incorporates an optoNCDT 1420 laser triangulation sensor. “A critical part of the ActiWheel system is to understand the lateral position of the wheel relative to the rail. Then we can control the wheel and avoid the flange making contact with the rail head,” explains Cooney. “We’re really using the sensors to confirm that our complex controllers are actually working correctly.”

The optoNCDT 1420 laser triangulation displacement sensor measures the lateral position of the bogie wheels relative to the rail head.

The optoNCDT 1420 laser triangulation displacement sensor measures
the lateral position of the bogie wheels relative to the rail head.

SET engineers built a special frame that lies beneath the wheel axle on the train. The optoNCDT 1420 sensors are mounted at a distance of 400mm from the rail head, just in front of the flange radius, pointing directly at the rail head. Measurement data from the sensors is output to the ActiWheel control system via 4-20mA.

“We initially approached Micro-Epsilon for a suitable sensor and were very impressed with the application engineer who demonstrated the optoNCDT 1420 sensor to us. The sensor met all our technical requirements in terms of its flexibility, resolution and robustness. We are measuring down to 0.1mm accuracy and lateral movement can be up to a maximum of 20mm,” states Cooney.

“The sensors have performed very well in the demonstrator project and on other projects where we’ve tested the ActiWheel system. They are reliable even in the harsh environment underneath a train, where dust, dirt and moisture are present. They operate reliably whether it’s a cold, wet, rainy day or a bright sunny day. In the demonstrator project, we didn’t even have to clean the sensors after a couple of thousand miles of testing.”

Adam Offord

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