Siemens invests in Wi-Tronix to expand digitalised services in rail sector


Siemens has made an equity investment in US-based remote monitoring specialist Wi-Tronix to strengthen its own digital predictive maintenance services for railways.

Both parties have agreed not to disclose the financial terms of the transaction.

The deal is expected to enable technological integration and facilitate further innovations within the rail sector.

Wi-Tronix specialises in the provision of remote monitoring, video analysis and predictive diagnostic systems for rolling stock and rail infrastructure which are designed to enable rail operators to procure critical data in real-time via its Software as a Service (SaaS) solution.

Siemens Mobility Division Customer Services CEO Johannes Emmelheinz said: “Wi-Tronix is a leading innovator in real-time monitoring for rail.

"The company has profound expertise in key technologies such as video analysis, providing unique information for both real-time and predictive applications.

"Partnering with developers of exceptional technologies is a key part of our strategy to deliver expansive digital services for predictive maintenance."

“Partnering with developers of exceptional technologies is a key part of our strategy to deliver expansive digital services for predictive maintenance.”

It is estimated that almost 12,000 locomotives across multiple countries worldwide are currently equipped with Wi-Tronix technology and connected with SaaS-based solutions.

Wi-Tronix president and chief technology officer Larry Jordan said: “Siemens shares our commitment to improving the world by making the transportation of people and goods safer, more reliable, and more efficient.

“This requires rail operators to have access to critical data, which supports both real-time decisions and predictive maintenance.”

Siemens already operates a global network of Mobility Data Services Centres in order to assess all data procured from several sensors and controllers on-board trains, locomotives and other assorted rail infrastructure.

These analyses help to predict system failures, as well as make necessary recommendations for scheduled maintenance periods.