Condition Monitoring · MINING
Surface processing plants that run on predicted condition, not calendar guesses.
Surface processing plants chain crushers, mills, conveyors and screens — and when any one degrades quietly, throughput and product quality drift before maintenance notices. The XMPro AO Platform fuses live sensor data from every line into a continuous picture of asset condition, ranked by predicted failure and synchronised across the plant.
What's getting in the way today.
Surface processing plants run continuously against abrasive material and tight throughput targets. Four pressures compound:
Reactive maintenance
Calendar-driven maintenance either over-services healthy equipment or misses degradation already underway — both cost throughput and crew time.
Unsynchronised line maintenance
Maintenance tasks across crushers, mills and conveyors rarely line up, so each intervention takes a bigger production bite than it needs to.
Hidden performance drift
Without continuous telemetry, performance loss hides in the aggregate — product quality drops and energy use climbs before anyone sees a cause.
Safety exposure
Equipment running outside safe operating limits creates personnel and process hazards that grow until they’re caught manually.
Surface Processing Plant Condition Monitoring — how it works.
A unified condition picture of every line in the plant — fed by the sensors already in service, modelled as a digital twin, and tied to predictive analytics that drive maintenance scheduling and alerting.
The platform integrates live sensor telemetry from equipment across crushers, mills, conveyors and screens — capturing the metrics that drive both throughput and condition. A digital twin of the plant gives reliability engineers a dynamic virtual representation for scenario analysis and maintenance planning. Predictive analytics anticipate component degradation and surface ranked recommendations with confidence scoring and time-to-action. Threshold breaches trigger automatic alerts to the right responder, and configurable dashboards drill from plant-level health into individual lines and assets so maintenance scheduling moves from reactive to predicted, synchronised across the plant.
*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 condition, synchronised maintenance
Maintenance windows align across lines because the platform sees the whole plant — not one asset at a time.
Throughput protected from quiet drift
Continuous telemetry surfaces performance loss before it shows up in aggregate output or product quality.
Safer operating envelope
Equipment running outside safe limits is flagged in real time, before the hazard becomes an incident.
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.
