Criticality Assessment of Wind Turbine Defects via Multispectral UAV Fusion and Fuzzy Logic

Pavlo Radiuk (), Bohdan Rusyn, Oleksandr Melnychenko, Tomasz Perzynski, Anatoliy Sachenko, Serhii Svystun and Oleg Savenko
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Pavlo Radiuk: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine
Bohdan Rusyn: Department of Information Technologies of Remote Sensing, Karpenko Physico-Mechanical Institute of NAS of Ukraine, 79601 Lviv, Ukraine
Oleksandr Melnychenko: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine
Tomasz Perzynski: Faculty of Transport, Electrical Engineering and Computer Science, Casimir Pulaski Radom University, 29, Malczewskiego St., 26-600 Radom, Poland
Anatoliy Sachenko: Faculty of Transport, Electrical Engineering and Computer Science, Casimir Pulaski Radom University, 29, Malczewskiego St., 26-600 Radom, Poland
Serhii Svystun: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine
Oleg Savenko: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine

Energies, 2025, vol. 18, issue 17, 1-27

Abstract: Ensuring the structural integrity of wind turbines is crucial for the sustainability of wind energy. A significant challenge remains in transitioning from mere defect detection to objective, scalable criticality assessment for prioritizing maintenance. In this work, we propose a novel comprehensive framework that leverages multispectral unmanned aerial vehicle (UAV) imagery and a novel standards-aligned Fuzzy Inference System to automate this task. Our contribution is validated on two open research-oriented datasets representing small on- and offshore machines: the public AQUADA-GO and Thermal WTB Inspection datasets. An ensemble of YOLOv8n models trained on fused RGB-thermal data achieves a mean Average Precision (mAP@.5) of 92.8% for detecting cracks, erosion, and thermal anomalies. The core novelty, a 27-rule Fuzzy Inference System derived from the IEC 61400-5 standard, translates quantitative defect parameters into a five-level criticality score. The system’s output demonstrates exceptional fidelity to expert assessments, achieving a mean absolute error of 0.14 and a Pearson correlation of 0.97. This work provides a transparent, repeatable, and engineering-grounded proof of concept, demonstrating a promising pathway toward predictive, condition-based maintenance strategies and supporting the economic viability of wind energy.

Keywords: defect criticality; fuzzy logic; artificial intelligence; multispectral fusion; sustainable energy; UAV inspection; wind turbine blades; YOLO; condition-based maintenance; structural health monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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