Prescriptive AI for
Steel Industry

Because most of them are not purely mechanical. A dedusting fan does not fail from bearing wear alone, it fails because material build-up shifts the rotor balance and 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 steel-specific failure logic through Dynamic FMEA, so a fault on a sinter cooler fan is evaluated against how sinter cooler fans actually fail, not against a generic deviation threshold.

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

Predictive maintenance forecasts that an asset is likely to fail. Prescriptive maintenance states which asset, which fault, which corrective action, and by when. In practice the difference is workload: a predictive alert still needs a reliability engineer to diagnose and decide, whereas a PlantOS™ prescription arrives already diagnosed and ready to convert into a work order.

Coverage spans the assets where mechanical and process faults compound: EOT Crane, blast furnace blowers and dedusting ID fans, continuous casters and caster mould pumps, hot strip and cold rolling mill drives, sinter plant cooler and exhaust fans, coke oven vapour compressors, Electric Arc Furnace, pellet plant ball mills, and cold mill recoilers. These operate under thermal load, abrasive conditions, and duty cycles where generic models perform worst.

Three, delivered together: reduced unplanned downtime, higher throughput on the lines that carry output, and lower cost per tonne through fewer emergency interventions, reduced spares consumption, and less energy waste on rotating equipment. Across deployments, PlantOS™ has digitalised 240 plants, prevented 19,050 equipment breakdowns, and eliminated 62,491 hours of unplanned downtime as of June 2026, per the PlantOS™ Digital Reporting System.

Each prescription carries the asset, the fault, the recommended action, and the timeframe, and is delivered to the maintenance team on mobile for work order creation and digital sign-off. Execution and outcome are logged, and validated outcomes feed back into the models that generated the prescription.

No. PlantOS™ integrates with the infrastructure already in the plant, ingesting data from existing historians, PLC and SCADA systems, data lakes, and installed sensors, alongside its own hardware where additional coverage is needed. Prescriptions are delivered into the existing CMMS and maintenance workflow, so the plant gains process context on its critical assets without a rip and replace programme.