Prescriptive AI for
Mining & Metals Industry

Because most of them are not purely mechanical. A slurry pump does not fail from impeller wear alone, it fails because feed density variation drives cavitation that accelerates that wear. Conventional condition monitoring reads the vibration signature and misses the process condition driving it, so the alert arrives late and without a cause.

Generic industrial AI applies one anomaly model across every asset in every industry. Vertical AI is built for the equipment and process conditions of a single sector. Infinite Uptime's PlantOS™ applies mining-specific failure logic through Dynamic FMEA, so a fault on a SAG mill pinion is evaluated against how SAG mill pinions actually fail under abrasive load, not against a generic deviation threshold.

PlantOS™ analyses vibration, thermal, and process data from the same asset simultaneously, then correlates the two signature types. A gear mesh reading on a SAG mill drive is interpreted alongside ore hardness, feed rate, and mill load, which separates a genuine developing fault from a normal response to a feed variation. The prescription names the mechanical fault and the process condition sustaining it.

Predictive maintenance tells you a SAG mill bearing might fail in 30 days. Prescriptive AI tells you what is wrong, for example an inner race defect at 2.4 mm/s velocity, what action to take, when to schedule it against the next planned shutdown, and what the production impact will be if you do not. In mining, where a single crusher or mill failure halts the comminution circuit and backs ore up for hours, that is the difference between a controlled intervention and a catastrophic stoppage. Prediction accuracy is 99.97 percent.

Coverage spans the assets where mechanical and process faults compound. In mining that includes SAG mills, ball mills, jaw and cone crushers, vibrating screens, grizzly feeders, slurry pumps, agitators, cyclone separators, ID fans, conveyor belts and rollers, stackers, reclaimers, and draglines. In metals processing it extends to cast house cranes, baking furnace fans, potline circulation blowers, rolling mill drives, and cooling tower systems. Each asset is mapped to models trained on the failure modes that occur in these conditions, including bearing degradation under abrasive slurry, crusher liner wear, and mill pinion gear faults.

Yes. PlantOS™ adapts across open-pit mining, underground mining, and mineral processing plants regardless of commodity, covering zinc, copper, aluminium, iron ore, gold, and coal. The vertical AI models are trained on equipment behaviour patterns common across these environments, from haul-road conveyors in open-pit operations to ventilation fans and hoisting systems underground.

Three, delivered together: reduced unplanned downtime, higher throughput across the comminution and processing circuit, and lower cost per tonne through fewer emergency interventions, reduced spares consumption, and less energy waste on rotating equipment.

Across Vedanta Group, one of the world's largest producers of zinc, aluminium, copper, and iron ore, PlantOS™ monitors 9,055 critical equipment units through more than 26,300 sensors across 77 plant sites and 489 areas. The platform maintains an MTBF of 5,206 hours and has driven 1,554 prescriptions to action, with an operator validation rate above 83 percent. At Hindustan Zinc Limited alone, PlantOS™ eliminated 5,039 hours of unplanned downtime and delivered over $700,000 in production savings across its mines and smelters.

Each prescription carries the asset, the fault, the recommended action, and the timeframe. Each one is digitally verified by a reliability engineer/plant head/maintenance manager before it reaches the plant floor, so it is contextually accurate for that asset, operating condition, and process stage. Execution and outcome are logged, and validated outcomes feed back into the models through the 99% Trust Loop.

No. PlantOS™ integrates with the infrastructure already in the operation, unifying data from PLCs, DCS historians, energy meters, and installed sensors alongside its own hardware where additional coverage is needed. Prescriptions are delivered into the existing maintenance workflow, so the site gains process context on its critical assets without a rip and replace programme.