OceanWind AI
Live across 4 farms · 278 turbines · 6.4M signals/min

Physics-informed AI for the offshore wind fleet.

Detect anomalies hours before failure. Forecast remaining useful life. Coordinate crews across the North Sea, Baltic, and Atlantic — from a single mission-grade console.

Active Turbines
278
across 4 farms
Anomalies Caught
1,284
last 30 days
Avg. Lead Time
47h
before failure
Fleet Availability
97.4%
+1.8% YoY
The Platform

From SCADA stream to maintenance dispatch — in one closed loop.

Edge & SCADA
OPC-UA, MQTT, and proprietary OEM protocols ingested at 25 kHz with sub-second pipeline lag.
Physics-Informed AI
Hybrid residual models combine first-principles turbine physics with deep representation learning.
Anomaly Detection
Subsystem-aware detectors with calibrated AI confidence and explainable residual signatures.
Health & RUL
Per-component health indices and remaining-useful-life forecasts with uncertainty bounds.
Predictive Maintenance
Auto-generated work orders, crew scheduling, and weather-window optimization.
MLOps at Scale
Versioned model registry, shadow deployments, drift monitoring, GPU autoscaling.
Built for mission-critical operations

Operate the fleet like a control room. Reason about it like a research lab.

  • Multi-region deployment with active-active failover
  • ISO 27001, IEC 61400-25 SCADA, NIS2-aligned audit
  • Sub-100ms inference at the edge for safety-critical loops
  • Open data fabric: Parquet, Iceberg, and time-series native
// live ingest
[06:14:02] WT-3321 · gearbox.vibration.hss = 4.82 mm/s ⚠ residual 4.2σ
[06:14:02] WT-3321 · gearbox.oil.particle_count = 22/19/15
[06:14:03] ANM-2847 detected · confidence 0.94 · subsystem=gearbox
[06:14:03] RUL-Forecaster predicting 14 days · uncertainty ±3
[06:14:04] WO-9112 generated · crew=Alpha · weather-window=2026-05-12
[06:14:04] ✓ pipeline ok · 6.4M msg/min · p95=38ms