Because most of them are not purely mechanical. A process pump does not fail from seal
wear alone, it fails because a change in feed concentration or temperature drives
corrosion and cavitation that 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 chemicals-specific failure logic through Dynamic
FMEA, so a fault on a synthesis gas compressor is evaluated against how those
compressors actually fail under process gas variation, not against a generic deviation
threshold.

PlantOS™ analyses vibration, temperature, pressure, and process data from the same asset
simultaneously, then correlates the two signature types. A rising vibration reading on a
reactor circulation pump is interpreted alongside feed concentration and temperature,
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: centrifugal and
process pumps, reciprocating and centrifugal compressors, synthesis gas and CO₂
compressors, reactor agitators, vacuum pumps, boiler feed pumps, cooling water pumps,
and ID and FD fans. These operate under corrosion, thermal load, and pressure conditions
where generic models perform worst.

Three, delivered together: reduced unplanned downtime, higher throughput on the trains
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 55 plants, prevented 2,462 equipment breakdowns, and eliminated
6,128 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 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.

By reducing unplanned equipment trips, PlantOS™ reduces the process upsets and emergency
shutdowns that carry the highest safety and regulatory exposure. Prescriptions are
validated by domain experts before reaching the plant floor, and every action is logged
with a digital audit trail, which supports compliance reporting. The hardware is ATEX
and IP68 certified for hazardous and corrosive areas.

Coromandel International and Indorama Ventures are among the producers using PlantOS™
for prescriptive maintenance and reliability. Coromandel uses PlantOS™ real-time alerts
across its plants to plan maintenance and avoid surprise breakdowns.