AI on the Rails: Smarter Wheel Monitoring Rolls Forward

Pilot systems stream real-time wheel data, pushing predictive rail maintenance closer to everyday reality

13 Feb 2026

For generations, railways have relied on routine inspections and trackside detectors to keep wheels in order. The work is methodical and largely effective. But it is periodic. Between checks, operators depend on experience, fixed schedules and a margin of caution.

Now they are being offered something different: constant attention.

A new class of onboard monitoring systems is moving from pilot schemes to early fleet deployments. Sensors mounted directly on rail vehicles collect continuous data on wheel slide, vibration and emerging faults. Instead of intermittent snapshots, engineers receive a steady stream of information about wheel health.

In controlled settings, research suggests accuracy approaching 98%. Yet railways do not operate in laboratories. Broader trials are under way as firms refine algorithms to distinguish genuine defects from the noise of vibration, debris and shifting weather. Steel wheels on steel rails produce plenty of both.

The push reflects a wider digital shift across the industry. Operators are investing in predictive maintenance, using sensor networks and artificial intelligence to track bearings, brakes and other costly components. Wheelsets, long monitored but rarely in real time, are becoming the next target.

In Europe, Greenbrier Leasing and Amsted Digital have launched joint wheel and brake monitoring programmes that stream onboard data directly to maintenance teams. The aim is to replace rigid inspection intervals with condition based decisions. Repairs would follow evidence of wear, not the calendar.

The commercial logic is clear. Labour costs are rising, safety scrutiny is tightening and rolling stock must spend more time earning revenue. Continuous data could allow earlier interventions, better workshop planning and more accurate forecasts of component life. Maintenance becomes less reactive and more precise.

Obstacles remain. Sensors must survive harsh vibration, temperature swings and constant grime. New data flows must be integrated into ageing maintenance systems. Cybersecurity and regulatory approval will influence the pace of adoption.

Even so, the direction of travel is evident. Railways, long defined by steel and timetables, are learning to trust software as well. Whether algorithms can match the industry’s hard won caution will determine how quickly they ride every train.