Project Description:
In 2025, Windrix carried out an integrated structural inspection project for a total of 28 wind turbines, consisting of GE 5.5-158 and GE 3.8-130 models. The project involved both external surface scanning using industrial drone systems and detailed internal blade analysis through a robotic inspection platform.
External Blade Inspection:
Industrial-grade drone technologies
High-resolution visual scanning and AI-assisted pre-analysis
Key Findings:
• Serial cracks
• TE/LE open bonding lines
• Lightning damage
• Seam seal deformations
• Hatch bolt looseness
• Leading edge erosion
Internal Blade Robotic Inspection – Technical Specifications:
Step-by-step internal structure scanning with 5.7K imaging and 360° camera
3D modeling using laser-assisted sonar and depth cameras
Millimeter-precision measurements, anomaly detection, and point cloud generation
AI-supported analysis for real-time crack and deformation marking
Inspection methodology compliant with ISO 9712 and IEC 61400-23 standards
High-efficiency inspections of up to 3 turbines per day
Highlighted Internal Damage Findings:
Lamination weakness and delamination
Cover cracks and retrofit connection issues
Foreign objects such as forgotten nuts or eyebolts inside the structure
Surface delaminations in retrofit zones
Light-permeable weak zones detected in various areas
Foreign Object Debris (FOD) findings
Project Added Value:
Holistic structural assessment through simultaneous analysis of internal and external data
Prioritized repair plans based on detected anomalies and cracks
Documentation of OEM-related defects and clarification of technical responsibility
Development of preventive maintenance strategies for the customer, contributing to long-term turbine lifespan and operational efficiency
Cost reduction and time optimization through autonomous inspection processes
Windrix makes potential risks in your turbine assets visible with its hybrid inspection capabilities, engineering strength, and AI-based analysis systems — ensuring business continuity through data-driven interventions.
