Windrover Case Study

Real Damage. Real
Results.

On 17 March 2026, Windrover detected a CAT4–CAT5 Surface Gap Damage through continuous acoustic monitoring. A drone inspection performed on 29 March 2026 confirmed the damage exactly where the system had identified it.

By detecting the defect four months before the scheduled annual inspection, the operator was able to intervene early, reduce maintenance costs by approximately 50%, and prevent further blade deterioration.

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1000+
Turbines
Monitored
40+
Blades
Saved
10
Countries
4.2M+
Minutes of
Blade Data
Case Studies

All Case Studies

A field-proven library of blade damage scenarios detected, classified, and tracked by Windrover across different turbine environments.

01
offshore Belgium

Lightning Strike Intelligence

Detected strike event, identified the affected blade, and classified severity for targeted inspection and faster decision-making.

Lightning Strike Intelligence
02
offshore Belgium

Ice Formation Detection

Detected early-stage icing conditions through blade behavior analysis, improving operational awareness and intervention planning.

Ice Formation Detection
03
Germany

Leading Edge Erosion Progression

Continuous erosion tracking and severity monitoring helped optimize repair timing and reduce maintenance uncertainty.

Leading Edge Erosion Progression
04
Scotland

Structural Crack Detection

Early-stage crack detected and monitored before escalation, enabling repair completion and avoiding failure.

Structural Crack Detection
05
offshore Belgium

Damage Progression Tracking

Tracked progression from CAT2 to CAT4 for smarter maintenance planning before failure.

Damage Progression Tracking
06
North Sea

Storm Operations Monitoring

Continuous monitoring throughout storm conditions to maintain visibility and avoid delays.

Storm Operations Monitoring
07
Multi-Site Fleet

Fleet-Wide Blade Intelligence

Centralized monitoring of fleet-wide blade health for better prioritization and resource allocation.

Fleet-Wide Blade Intelligence
08
Remote Wind Farm

Autonomous Monitoring

Continuous monitoring using adaptive power management for reduced site visits and 24/7 visibility.

Autonomous Monitoring
09
Operational Wind Farm

Acoustic Anomaly Detection

Abnormal acoustic signatures identified during operation to initiate investigation before visible deterioration.

Acoustic Anomaly Detection
Windrover In Action

From Detection To Decision


01

Detect

AI scans blade imagery across environments to detect anomalies.

02

Classify

Findings are classified by type, severity, and location.

03

Track

Conditions are tracked over time to monitor progression.

04

Verify

AI and expert review confirm accuracy and reduce false positives.

05

Repair

Actionable insights guide maintenance planning and prioritization.

06

Failure Prevented

Proactive intervention prevents failure, reduces downtime, and maximizes uptime.

i

Windrover does not stop at detection.

Global Operations

Proven Across Offshore and Onshore Wind Farms

Live CoverageMonitoring 24/7 In Real Operating
Conditions
Case Study MarkersClick A Location To Explore Real
Customer Results

Deployment Locations
10+

Deployment Intensity

LowHigh
1000+
TURBINES
80+
TURBINE MODELS
10
COUNTRIES
4.2M+
MINUTES OF BLADE DATA
40+
BLADES SAVED
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