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Energy & ChemicalsCase study

Predictive Equipment Maintenance for an Energy & Chemicals Enterprise

IoT data acquisition as the base and AI algorithms as the core: predictive maintenance upgrading reactive repair into early warning.

Predictive Equipment Maintenance for an Energy & Chemicals Enterprise

Background & solution

We deployed IoT data acquisition and edge processing for critical units, ingested DCS/SCADA history, built equipment health assessment and fault-warning models, and delivered visual monitoring boards with alerting integrated into the existing production management system.

Outcomes

✓ Unplanned downtime of critical equipment dropped significantly; inspection and maintenance labor is better focused; equipment data went from dormant records to decision inputs.