WindSights

AI-Powered Predictive Maintenance for Wind Farms

Optimize output and minimize the levelized cost of energy for every turbine in your fleet — combining SCADA telemetry, meteorological data, and aerial imagery with Databricks AI to predict faults before they happen.

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What is WindSights?

Wind turbines operate for long hours under exposed conditions, withstanding a wide range of dynamic loads driven by intermittent wind speeds and direction changes. Equipment wear is inevitable — but catastrophic failure doesn’t have to be. Today, most wind farm operators rely on time-based maintenance schedules and reactive repairs, leaving significant revenue and asset life on the table.

WindSights is an AI accelerator built on the Databricks Data Intelligence Platform that combines high-frequency SCADA telemetry (via AVEVA CONNECT), meteorological data, GIS, and optional aerial imagery into a unified Lakehouse. Custom deep learning models identify complex, multi-variable failure patterns that traditional rule-based systems miss entirely — surfacing fault predictions, component health scores, and prioritized maintenance queues in an easy-to-use Databricks App before downtime occurs.

Real Impact for Utilities

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35–50% Reduction in Turbine Downtime

Predictive maintenance keeps turbines generating and maximizes revenue by addressing faults before unplanned outages occur.

Lower O&M Costs

Condition-based interventions replace expensive emergency responses with planned, efficient repairs — reducing overall O&M costs across the fleet.

Extended Asset Lifespan

Early detection of component degradation defers costly capital replacements and extends turbine life through proactive health monitoring.

Improved Technician Safety

Pre-planned maintenance visits reduce technician exposure to hazardous conditions and cut mean time to repair across the fleet.

What You Can Do With WindSights

Predictive Fault Detection

Custom deep learning models identify complex, multi-variable failure patterns across gearboxes, blades, generators, and pitch systems — catching issues weeks before they cause downtime.

Natural Language Operations Interface

Agentic AI powered by Genie and custom DSPy deployments lets operators ask plain-language questions — “Which turbines are at highest risk this week?” — and get instant, governed answers.

Unified Asset Health Dashboard

A single interactive view surfaces fault predictions, component health scores, alert queues, and maintenance priorities across every turbine in your fleet — built on Databricks Apps.

Meteorological & Aerial Data Fusion

Correlates SCADA telemetry with high-resolution weather patterns and optional drone imagery for a more comprehensive system view — especially valuable for blade and structural health monitoring.

AVEVA CONNECT Integration

High-frequency SCADA data ingestion via AVEVA CONNECT captures early warning signs of component degradation at the telemetry level, feeding directly into the Databricks Lakehouse.

Automated Maintenance Prioritization

Writeback capabilities in the Databricks App allow maintenance teams to log decisions, close work orders, and feed findings back into the model — improving accuracy with every intervention.