Hexagon and Track Forward Launch AI Rail Maintenance Tool
The partnership integrates optical measurement and AI analytics to optimize wheel maintenance intervals for global rail operators.
Updated on Oct. 16, 2026 in Artificial Intelligence
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Hexagon and Track Forward have announced a partnership to integrate their optical measurement and AI analytics tools for rail wheel maintenance. The collaboration combines Hexagon's CALIPRI system with Track Forward's WheelSense platform to track wheel wear and optimize maintenance schedules.
Why it matters
By automating the monitoring of wheel wear, this integration helps rail operators move toward predictive maintenance models to lower operating costs and increase fleet availability. It responds to rising industry pressure for higher operational uptime without increasing maintenance expenditures.
The solution utilizes CALIPRI for non-contact optical measurement of wheelsets during operation, paired with WheelSense algorithms that process these datasets to identify wear trends. This replaces manual inspection cycles with continuous data-driven profile monitoring.
The players
Hexagon
An industrial technology company specializing in sensor and software solutions for manufacturing and infrastructure, known for their CALIPRI optical measurement systems.
Track Forward
A provider of specialized analytics software for the rail industry, developer of the WheelSense AI platform for maintenance optimization.
The details
CALIPRI sensors capture high-resolution geometry data from wheels while trains are in service, eliminating the need for stationary diagnostic stops. This measurement data is then passed to the WheelSense platform, an AI-driven analytics suite that runs trend analysis to predict when wheel profile degradation will exceed safety thresholds. By tracking the rate of material loss over time, the system enables operators to replace or turn wheelsets only when necessary rather than on a fixed schedule.
Timeline
October 16, 2026: Official announcement of the technology partnership.
The Tech Race
This partnership marks a significant consolidation of diagnostic hardware and predictive software in the rail sector. It pits combined diagnostic offerings against traditional manual inspection methods and legacy interval-based maintenance programs.
Rail operators can begin integrating this combined system into existing maintenance workflows to manage fleet health. Availability and deployment timelines remain dependent on specific fleet implementation strategies and current sensor infrastructure.
The takeaway
This collaboration signals a shift toward data-dependent rolling stock maintenance in global rail operations. Operators should watch for forthcoming case studies detailing the reduction in wheelset lifecycle costs following deployment.
Further reading
For broader trends in industrial diagnostics, explore Artificial Intelligence.
Source note: This article includes information reported by Railway-News.
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