Case Study: Internal Wind Turbine Blade Inspection Using Nexxis Crawler & Elios 3
Client
Confidential (Multinational Renewable Energy Provider)
Project Location
South Australia — Onshore Wind Farm
Project Overview
The client required a full internal inspection of wind turbine blades to assess structural integrity, identify early-stage damage, and prevent costly failures. Traditional rope access and manual methods posed safety risks, limitations in access, and significant downtime.
Scope of Work
Conduct detailed visual and LiDAR inspections inside 60m+ blade structures
Capture high-resolution imagery, 3D maps, and defect analytics
Minimise turbine downtime and technician risk
Provide structured reporting suitable for long-term asset lifecycle planning
Technology Deployed
Nexxis Pipe Crawler (Modified for Blade Access)
Equipped with dual side-mounted GoPro 360s and forward-facing 64MP camera
High-output Cree LED lighting system
Integrated Raspberry Pi control and Livox 3D LiDAR mapping
Remote operation from outside the blade structure
Elios 3 GE Drone (Flyability)
Deployed for supplementary mapping inside tight blade curves
Real-time SLAM LiDAR mapping
Collision-tolerant carbon cage design for narrow access
Dust-resistant lighting and payload design for confined dusty interiors
Key Achievements
100% access coverage inside 35 turbines with zero human entry
Mapped internal blade geometry in 3D for structural modeling
Identified 18 early-stage laminate delaminations and 6 root connection anomalies
Reduced inspection time per turbine from 4 hours to 45 minutes
Zero incidents, with full data delivery to GE Asset Management platform
Deliverables
Full 3D Blade Interior Point Cloud Models (Elios 3 LiDAR & Crawler LiDAR)
Annotated HD imagery of all defects with timestamped metadata
Condition rating per blade (1–5 scale with RAG status)
Engineering-ready reports for asset maintenance planning
Client Outcome
“The data clarity and internal access your team achieved has fundamentally changed how we assess blade health. We now have a digital record of the full interior for future AI-based defect comparison.”
— Lead Engineer, Wind Asset Management Team
