AI & Analytics
Answers, not just dashboards — with the physics to prove them.
TwinEdge analytics starts with physics-based asset models and process context, then adds anomaly detection, predictive maintenance, machine learning, and approved work-order action. Each layer explains what is happening, what will happen next, and what maintenance should do.
Intelligence Pipeline
Sensor data flows through physics models, process context, anomaly detection, predictive algorithms, and into maintenance workflows with evidence and approval controls.
Anomaly Detection
Multi-algorithm consensus engine grounded in physics operating ranges, asset behavior, and live telemetry to catch equipment issues before traditional threshold alarms.
Physics-Based Analytics
First-principles physics models that calculate real-time efficiency, operating point deviation, cavitation risk, thermal performance, and failure risk from raw sensor data.
Predictive Maintenance
Physics-informed machine learning models predict remaining useful life, detect degradation trends, and recommend maintenance windows based on actual equipment condition and operating load.
Automated Work Orders
When physics or ML models detect an actionable condition, Operations Intelligence can create a reviewed work order with diagnosis, failure mode, recommended action, and parts list.
Energy Optimization
Continuous energy waste detection across pumps, compressors, chillers, and AHUs. Identifies off-BEP operation, fouling losses, and scheduling inefficiencies in real time.
Online Condition Assessment
Physics and telemetry-driven condition scoring helps prioritize inspections by risk, evidence, operating envelope deviation, and asset health. Physical visits become targeted and justified.
Digital Twin Engine
Compose asset physics into process-level digital twins. Wire models into a directed graph and run what-if scenarios on the entire system.
Why TwinEdge Analytics?
Most platforms give you dashboards. TwinEdge gives you asset and process answers with the physics to back them up.
Physics First, ML Second
Physics models explain why something is happening: off-BEP operation, fouling, cavitation, thermal drift, hydraulic imbalance, or abnormal degradation. ML models predict when it will get worse.
Edge-Powered Intelligence
Edge analytics support low-latency inference, buffering, and local alerts when cloud connectivity is limited. Cloud services aggregate fleet-wide patterns and governed recommendations.
Closed-Loop to Ops Intel
Analytics do not stop at dashboards. Physics-based diagnosis, severity, recommended action, and parts context flow into AssetOps planning and approved work execution.
Transparent, Not Black-Box
Every anomaly score, prediction, and recommendation traces back to specific sensor readings, model inputs, physics equations, and operating-envelope assumptions your engineers can verify.
Physics-Grounded Intelligence That Acts, Not Just Alerts
From sensor to diagnosis, prediction, recommended action, and approved work in one pipeline. Physics-based, edge-powered, and operations-connected.