Predictive Maintenance · MINING
Predictive maintenance across the whole mobile fleet — not just haul trucks.
Haul trucks, dozers, drills, loaders and graders all live the same harsh duty cycle but each carries different telemetry, OEM systems and failure modes. The XMPro AO Platform unifies sensor data and OEM error codes across mixed-fleet mobile assets, predicts failure mode by mode, and schedules maintenance to minimise production disruption.
What's getting in the way today.
Mobile asset reliability across a mixed mining fleet is a coordination problem. Six pressures compound:
Harsh duty environments
Continuous exposure to abrasive materials, vibration and heavy loading accelerates wear across engine, drivetrain, hydraulics and structure.
Mixed fleet, mixed signals
Different OEMs, ages and technology stacks make standardising maintenance triggers across the fleet hard — every asset class behaves differently.
Downtime scheduling
Strategic timing of maintenance to fit the mine plan without stopping production is a continuous balancing act.
Telemetry overload
Modern mobile assets generate vast streams of operational data; without ranking and routing, the signal hides in the noise.
Compliance and safety
Maintenance lapses on mobile equipment carry both regulatory and personnel-safety consequences — particularly in remote operations.
Remote-site resource constraints
Scarce crews and spare parts at remote operations make misallocated maintenance time disproportionately expensive.
Mining Mobile Asset Predictive Maintenance — how it works.
A unified condition picture of every mobile asset across the fleet — fed by OEM telemetry, ranked by predicted failure, and tied directly to maintenance scheduling and work-order creation.
The platform integrates sensor and OEM error-code data continuously across mixed-fleet mobile assets — engine telemetry, hydraulic pressure and fluid quality, tyre pressure, power output, payload weight, idle versus run time. ML models analyse this telemetry per asset class to predict component failures, calculate Remaining Useful Life and surface specific failure modes (engine health drop, hydraulic anomaly, tyre pressure deviation) with confidence scoring. A digital twin lets reliability engineers simulate operating scenarios for each asset type. Threshold breaches generate ranked recommendations with parts, crew and severity attached, with work-order creation flowing into the CMMS. Operators see live geo-location, fleet health, recent recommendations and per-asset drill-down on configurable dashboards.
*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.
One condition view across the fleet
Haul trucks, dozers, drills and loaders converge into one operational picture — even across different OEMs and ages.
Maintenance aligned to the mine plan
Predicted failure windows let maintenance slip into planned slots without disrupting the production schedule.
Scarce crews spent where they matter
Ranked recommendations focus remote-site crew time on the asset and failure mode that actually drive downtime.
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.
