Condition Monitoring · TRANSPORT & LOGISTICS
Track geometry monitored as a continuous safety signal.
Most derailments trace back to subtle track-geometry drift — gauge widening, alignment deviation, twist building between scheduled inspections. The XMPro AO Platform fuses real-time geometry-sensor data with GPS context and historical maintenance records to predict track issues before they become derailment risks.
What's getting in the way today.
Rail operators face three compounding pressures that fixed-interval inspection cannot resolve:
Early anomaly detection
Subtle changes in track geometry — gauge, alignment, elevation, twist — develop between scheduled inspections and accumulate into derailment risk if no continuous measurement runs.
Efficient maintenance scheduling
Track maintenance must be optimised against operational disruption — blanket inspection is expensive, skipping is risky.
Data integration and analysis
Vast volumes of track-measurement and wheel-wear data must be integrated and analysed to make informed maintenance decisions — beyond manual analyst capacity.
Derailment Prevention via Track Geometry — how it works.
A live picture of track geometry across the network — fed by the geometry and GPS sensors already deployed, with ranked recommendations feeding directly into maintenance workflows.
The platform ingests geometry-sensor data (gauge, alignment, elevation, twist) and GPS telemetry from trains continuously, combining it with historical work-order data and contextual asset information. ML models including anomaly detection and remaining-useful-life prediction flag immediate high-risk deviations against predefined thresholds and forecast track-section longevity. Recommendations route to maintenance teams with deviation values, gauge, elevation, twist, timestamp and track-section identifier, ready for work-request creation. An interactive map of the rail network shows real-time GPS locations of trains, crossings, lines, maintenance vehicles and substations with colour-coded status icons, and per-segment drill-downs render 2D and 3D track models with predicted issues marked.
*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.
Predicted geometry deviations
Track issues surface ahead of derailment risk so maintenance plans around interventions rather than reacting to incidents.
Condition-based maintenance
Track work moves from fixed-interval to condition-based, reducing cost and minimising operational disruption.
Safer rail operations
Early detection of gauge, alignment and twist drift directly reduces derailment exposure across the network.
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.
