Condition Monitoring · TRANSPORT & LOGISTICS
Catch bogie wear before it becomes a safety event.
Wheel wear, axle load, bearing temperature and bogie vibration drift slowly — until they don’t. The XMPro AO Platform fuses live bogie telemetry across the fleet into a continuous health picture, predicts component failure modes, and shifts maintenance from fixed-interval to actual condition.
What's getting in the way today.
Rail operators carry two non-negotiable obligations — safety and on-time service — against an aging bogie fleet. Three pressures compound:
Early wear and failure detection
Identifying early signs of wear in wheels, bearings, axles and suspension is essential to prevent derailment and service disruption.
Maintenance scheduling
Fixed-interval service either over-services healthy bogies or misses degradation already underway — both undermine the safety case and the timetable.
Unscheduled repairs
Reactive repairs ripple across the schedule, take crew off planned work and amplify operational cost.
Bogie Health Monitoring — how it works.
A continuous picture of bogie condition across the fleet — fed by the sensors already on the bogie, modelled as a digital twin, and tied to predicted failure modes that drive maintenance scheduling.
The platform integrates wheel-wear, axle-load, bearing-temperature and bogie-vibration sensors continuously across the fleet. ML models analyse telemetry to predict failure modes — wheel-flange wear, bearing degradation, suspension-spring fatigue — with confidence scoring. A digital twin of the bogie supports wear-pattern simulation and intervention planning. Configurable dashboards drill from a fleet-level overview down to individual bogie state, with real-time alerts when thresholds are breached. Predictive insights replace fixed-interval service with condition-based maintenance scheduling.
*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.
Condition-based maintenance
Bogie service moves from fixed-interval to predicted wear — freeing depot capacity and extending component life.
Safer operating envelope
Degradation patterns are flagged before they become a derailment risk, with auditable evidence behind the call.
Schedule resilience
Maintenance windows move into planned slots rather than emergency response, protecting the timetable.
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.
