Prescriptive AI for Tire & Rubber Plants

Because most of them are not purely mechanical. A Banbury mixer does not fail from bearing wear alone, it fails because compound viscosity and thermal load raise drive torque 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 tire-specific failure logic through Dynamic FMEA, so a fault on a Banbury mixer drive is evaluated against how those drives actually fail under thermal and compound load, not against a generic deviation threshold.

PlantOS™ analyses vibration, temperature, load, and process data from the same asset simultaneously, then correlates the two signature types. A rising drive-torque reading on a mixer is interpreted alongside compound temperature and batch conditions, which separates a genuine developing fault from a normal response to a process change. 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. At Tornel, PlantOS™ prescribed a corrective alignment on Banbury mixer Gearbox 2 that let the team act before it caused major downtime.

PlantOS™ covers the full plant, matched to how critical each asset is. Your production-critical assets, the extruders and Banbury mixers where a failure stops the line, are covered by AI Shields, the deep-domain models built for exactly those machines. Standard and critical rotating equipment across mixing, milling, calendering, and curing is covered through wired and wireless sensing, piezoelectric or MEMS, so you match coverage to the asset and the budget. And balance-of-plant equipment, the chillers, cooling pumps, and fans, is covered by the self-powered, wireless InfiSense 3XT with no gateways or cabling.

Three, delivered together: reduced unplanned downtime, higher throughput on the lines carrying output, and fewer rejects through earlier detection of the faults that cause cure and profile defects. Across deployments, PlantOS™ has digitalised 33 plants, prevented 1,722 equipment breakdowns, and eliminated 7,508 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, email, and 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, and installed sensors alongside its own hardware 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.

Many tire defects trace back to a process or equipment deviation that condition monitoring alone does not connect: a mixer running hot, an extruder screw wearing, a curing press drifting off recipe. PlantOS™ reads the mechanical and process signatures together and prescribes the correction before the deviation reaches the product, which reduces cure and profile defects and the scrap they generate.

CEAT, Tornel, and MRF are among the tire producers using PlantOS™ for prescriptive maintenance. At Tornel in Mexico, PlantOS™ vibration alerts and prescriptive recommendations, including a corrective alignment on a Banbury mixer gearbox, have enabled the team to act early and prevent major downtime.