Nokia has partnered with engineering and R&D services (ER&D) provider Altran to jointly develop a streamlined, predictive maintenance solution of rolling stock for railway operators.

The solution will be developed combining Nokia’s expertise in Internet of Things (IoT), networking and analytics with Altran’s know-how in application, analytics and system integration for railway operations.

It will be designed to offer predictive maintenance support for rolling stock from train manufacturers and minimise maintenance costs for the operators.

Furthermore, the new solution will feature smart sensors to monitor and collect data on the condition and performance of rolling stock, as well as wireless network and IoT connections to combine and transmit data.

Once developed, the adaptive IoT platform will be able to manage several sensors and other devices. The solution will also serve as an advanced analytics engine to interpret data and suggest preventative maintenance measures.

It has been designed to improve train reliability by up to 30%, reduce delays and cancellations by 20%, and lower maintenance man-hours by 10%.

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By GlobalData
“Unexpected failures in rolling stock have a huge impact on the operational performance of rail operators.”

Nokia Global Transportation Segment leader Jochen Apel said: “Unexpected failures in rolling stock have a huge impact on the operational performance of rail operators.

“The traditional way of overcoming this is to perform heavy preventative maintenance.

“The revolutionary approach the Nokia and Altran are taking can greatly reduce this effort and deliver significant cost savings to railway operators.”

With the new agreement, Nokia aims to diversify and expand its footprint outside its traditional telecommunications business.

AltranRail and Transport Global Industry director Mike Greenan said: “Predictive maintenance is an opportunity for new and for legacy trains.

“The combination of Nokia and Altran can help introduce and take advantage of modern technologies to increase the operational performance of the train.”