Sensor and telemetry architecture
Across vibration, current, temperature, acoustic, pressure, and process variable streams.
Asset health intelligence, engineered for the plant.
Equipment failure is rarely sudden. It is preceded by signals that experienced engineers have always read in vibration, current, temperature, and sound - and that modern instrumentation can now record continuously.
Entiovi engineers predictive maintenance systems that turn those signals into reliable, operational asset health intelligence: early warning of degradation, prediction of failure mode and time horizon, and a maintenance posture that shifts from reactive to planned.
Entiovi designs and builds predictive maintenance solutions across rotating equipment, process units, and production-critical assets. The work spans sensor architecture, time-series engineering, model development, and integration with the maintenance systems the plant already runs - CMMS, EAM, and work order workflows.
Models are calibrated against real failure history, not theoretical degradation curves, and validated through phased deployment alongside reliability engineering teams. The objective is not a higher F1 score on a benchmark. The objective is a maintenance posture that costs less, prevents more failures, and earns the trust of the people responsible for the plant.
Across vibration, current, temperature, acoustic, pressure, and process variable streams.
With feature engineering, seasonality handling, and degradation modelling at industrial scale.
In supervised, unsupervised, and hybrid forms, calibrated to failure history.
For local inference where bandwidth, latency, or air-gap constraints require it.
With predictions surfaced inside the maintenance workflow rather than parallel to it.
Covering failure modes, criticality weighting, and outcome tracking.
Motors, pumps, compressors, fans, gearboxes.
Across utilities and production trains.
Engineered for the throughput and uptime the line depends on.
For performance and degradation.
Across multi-site operations.
Reduced unplanned downtime, planned maintenance windows aligned to actual asset condition, lower maintenance cost without compromising reliability, fewer secondary failures, and a measurable shift in the maintenance posture from reactive to evidence-driven.
Engagement starts with asset criticality assessment and failure history review, followed by a calibrated sensor and data architecture, model development on real plant data, and integration with the maintenance workflow. Once deployed, model performance is monitored against actual events - false positives, missed failures, and operator response - with a defined review cadence and a reliability engineering owner.
Predictive maintenance pays off when it changes how the plant operates - not when it produces another dashboard. Entiovi engineers for that change from the first sensor decision onward.
Entiovi's team will assess asset criticality, failure history, sensor footprint, and the maintenance workflow - and scope the first deployment against the operational outcomes the plant actually needs.