A UAV-Mounted Sensor Network for Close-Range Inspection of Wind Turbine Rotor Blades

2026-06-19

AI Summary

This study addresses the challenge of high-precision, close-range defect inspection of offshore wind turbine blades, which is hindered by their large dimensions and remote locations, as well as the limited reliability of existing drone-based systems in defect-type classification. To overcome these limitations, this work proposes a drone-mounted multimodal sensor fusion approach that, for the first time, integrates co-calibrated industrial RGB cameras, passive thermal infrared cameras, and a custom millimeter-resolution 3D scanner within a unified coordinate frame. This integration enables spatially aligned and synchronized acquisition of geometric, color, and thermal data, effectively mitigating motion-induced disturbances while balancing wide field-of-view coverage with high-fidelity 3D perception. Preliminary experiments in laboratory settings have successfully demonstrated synchronized multimodal data collection and point cloud reconstruction, establishing a foundational framework for future aerial inspection missions.

Abstract

Inspection of offshore wind turbine rotor blades is critical for predictive maintenance to maximise efficiency and extend operational lifetime. However, it remains a challenging task due to remote locations, large structural dimensions, and the limitations of current UAV-compatible sensor systems. While existing approaches can detect certain types of surface anomalies, reliable classification of defect types often remains a manual and error-prone process. This paper presents the design of a UAV-mounted multimodal sensor network combining an industrial RGB camera, a passive thermal infrared camera, and an in-house developed 3D scanner. All sensors are co-calibrated into a common coordinate frame, enabling spatial superimposition of geometric, colour, and thermal data. The system is designed to operate at close range, addressing three fundamental sensing challenges: platform motion, large field of view, and millimetre-level measurement accuracy. Preliminary laboratory results demonstrate synchronised multi-sensor acquisition and initial point cloud reconstructions, forming the basis for future airborne inspection trials.

Problem

• wind turbine inspection
• UAV-mounted sensors
• defect classification
• close-range sensing
• multimodal sensing

Innovation

• multimodal sensor fusion
• UAV-based inspection
• co-calibration
• 3D scanning
• wind turbine blade