Case Study: Windmill achieves faster, cheaper wind turbine fault detection with Nanonets

Fault Detection in Wind Turbines

Windmill, a wind turbine operator, needed a faster and safer way to inspect turbines and analyze aerial images for faults. Manual inspections using ropes and platforms were time-consuming, costly, and limited to just a few turbines per day, while manually reviewing high-resolution drone images was tedious and error-prone. Nanonets provided an AI-powered inspection solution to automate the image analysis phase of the drone-based inspection workflow.

With Nanonets’ custom machine learning models, Windmill could automatically identify and categorize turbine faults from inspection images at high speed and low cost. The impact was significant: image analysis increased from 1 turbine per manhour to 100 turbines per hour, and analysis cost dropped from $55 to $1 per turbine.