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AI & Analytics

Five layers of intelligence — from physics-based equipment models to multi-algorithm anomaly detection to automated work order generation. Each layer feeds the next. All running on the edge.

Intelligence Pipeline

Sensor data flows through physics models, anomaly detection, predictive algorithms, and into your maintenance workflow — automatically.

Sensors
Physics Models
Anomaly Detection
Predictive ML
Work Orders

Anomaly Detection

Multi-algorithm consensus engine that catches equipment issues 2-8 hours before traditional threshold alarms. Five algorithms running in parallel to minimize false positives.

5 algorithms<50ms latency85% fewer false positives

Physics-Based Analytics

First-principles physics models that calculate real-time efficiency, operating point deviation, cavitation risk, and thermal performance from raw sensor data.

9 equipment modelsPump curvesNPSH tracking

Predictive Maintenance

Machine learning models that predict remaining useful life, detect degradation trends, and recommend optimal maintenance windows based on actual equipment condition.

RUL predictionDegradation trending73% fewer failures

Automated Work Orders

When physics or ML models detect an actionable condition, a work order is auto-created in Operations Intelligence with diagnosis, recommended action, and parts list — no manual entry.

Auto-generatedParts pre-stagedFull audit trail

Energy Optimization

Continuous energy waste detection across pumps, compressors, chillers, and AHUs. Identifies off-BEP operation, fouling losses, and scheduling inefficiencies in real time.

15-30% savingsContinuous monitoringROI tracking

Online Condition Assessment

Telemetry-driven condition scoring eliminates 73% of scheduled inspections. Physical visits only when sensors can't assess — targeted, specific, and justified by data.

73% fewer inspections100% fleet coverageCamera AI

Digital Twin Engine

Compose these analytics into process-level digital twins. Wire models into a directed graph and run what-if scenarios on the entire system.

Model graphScenario API<100ms

Why TwinEdge Analytics?

Most platforms give you dashboards. We give you answers — with the physics to back them up.

Physics First, ML Second

Physics models explain why something is happening (12% off BEP, fouled heat exchanger). ML models predict when it will get worse. Together they eliminate both false positives and blind spots.

Edge-Powered Intelligence

All analytics run on the edge for sub-second response. No round-trip to the cloud. No dependency on internet connectivity. The cloud aggregates fleet-wide patterns — but the edge acts.

Closed-Loop to Ops Intel

Analytics don't just generate dashboards. They generate work orders with diagnosis, severity, recommended action, and parts list. The gap between detection and action is eliminated.

Transparent, Not Black-Box

Every anomaly score, prediction, and recommendation traces back to specific sensor readings and physics equations. Your engineers can verify and trust the outputs.

Intelligence That Acts, Not Just Alerts

From sensor to work order in one pipeline. Physics-based, edge-powered, operations-connected — no manual steps in between.