Condition Monitoring · TRANSPORT & LOGISTICS
Wheels and tracks that flag wear before it puts a train at risk.
Abnormal wheel and track wear is one of the leading contributors to derailment risk, and fixed-interval inspections either miss degradation or over-service healthy assets. The XMPro AO Platform monitors wheel profile, axle load, vibration and track condition continuously, predicts remaining useful life, and ranks maintenance work by safety and operational impact.
What's getting in the way today.
Rail asset reliability sits at the intersection of safety, throughput and capital. Three pressures compound:
Derailment risk
Abnormal wear in wheels and tracks raises derailment exposure — failure to detect it early is both a safety and a regulatory issue.
Maintenance efficiency
Fixed inspection intervals either over-service safe assets or miss degradation already underway, eating crew time and capital both ways.
Operational downtime
Unplanned maintenance and repairs disrupt timetables and freight commitments, with knock-on cost across the network.
Rail Wheel & Track Wear Monitoring — how it works.
A continuous picture of every wheel and track segment — fed by the sensors already on the rolling stock and the network, ranked by predicted failure and time-to-action.
The platform integrates wheel profile, wheel vibration, track profile, axle load, bearing temperature and GPS data continuously from sensors on trains and tracks. ML models analyse this telemetry to detect anomalies in wear pattern and forecast remaining useful life for wheel components, with confidence scoring. A real-time interactive map shows trains, crossings, lines, maintenance vehicles and substations colour-coded by operational state — with drill-down into per-asset analysis including 2D/3D models that highlight specific defect locations. Threshold breaches (wheel wear depth, vibration amplitude, bearing temperature) trigger ranked recommendations with event data and work-request creation, feeding maintenance scheduling and regulatory compliance reporting directly.
*Illustrative dashboards from the platform. Layout, signals and decision points are scoped per site.
Scope this for your operation.
Tell us about your fleet, your control maturity and the lever that matters most. We’ll map this use case to your starting point.
What this looks like in operation.
Safer rail operations
Abnormal wear surfaces while it’s still recoverable, reducing derailment exposure and the regulatory consequences that follow.
Condition-based maintenance
Inspection moves from fixed interval to actual condition, so crew time goes to the assets that need it.
Predictable network availability
Predicted failure modes let work move into planned windows rather than emergency response, protecting timetable and freight commitments.
Built for these industries.
Other solutions you might explore.
Not a concept. In production.
XMPro is deployed at Tier 1 global operators across asset-intensive and mission-critical industries — delivering measurable results across predictive maintenance, process optimisation and operational intelligence.
Now pushing the frontier.
MAGS agents are achieving what no other industrial platform has demonstrated — sustained autonomous operations at enterprise scale.
