Prescriptive AI for
Cement Industry

Because most of them are not purely mechanical. A preheater fan does not fail from bearing wear alone, it fails because cyclone coating and material build-up shift the rotor balance and accelerate 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 cement-specific failure logic through Dynamic FMEA, so a fault on a kiln girth gear is evaluated against how kiln girth gears 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 reading on a kiln main drive is interpreted alongside thermal load and feed 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. Prediction accuracy is 99.97 percent.

Coverage spans the assets where mechanical and process faults compound: rotary kilns, kiln main drives, girth gears and pinions, ball mills, vertical roller mills, roller presses, preheater cyclones and preheater fans, clinker cooler fans, raw mill and cement mill fans, bag filter fans, bucket elevators, compressors, and more. These operate under thermal load, abrasive dust, and duty cycles where generic models perform worst.

Three, delivered together: reduced unplanned downtime, higher throughput on the lines carrying 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 162 plants, prevented 9,131 equipment breakdowns, and eliminated 33,501 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 the dashboard on desktop or mobile for work order creation and digital sign-off. 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 plant, unifying data from PLCs, DCS historians, energy meters, SAP and other ERP systems, MES, CMMS, and QMS logbooks, alongside its own sensors where additional coverage is needed. Prescriptions are delivered into the existing maintenance workflow, so the plant gains process context on its critical assets without a rip and replace programme.

First prescription is typically delivered within two weeks of installation. Deployment usually begins on a single critical line, often the kiln or the raw mill, and scales plant-wide once outcomes are validated by the plant's own reliability team.

PlantOS™ hardware is rated for surface temperatures up to 150 °C and operation down to 2 rpm, and is CE, RoHS, ATEX, and IP68 certified for the dust, heat, and hazardous-area conditions found around kilns, mills, and preheater towers.

Star Cement and Southern Province Cement Company (SPCC) are among the producers using PlantOS™. Star Cement achieved 10x ROI in under six months across four plants, saving 46 hours of downtime and gaining 10 TPH of throughput. SPCC achieved 9x ROI in under six months with $175,105 in annual maintenance savings.