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

Radiuk, Pavlo; Rusyn, Bohdan; Melnychenko, Oleksandr; Perzynski, Tomasz; Sachenko, Anatoliy; Svystun, Serhii; Savenko, Oleg

doi:10.3390/en18174523

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

by

Pavlo Radiuk

^1,*

,

Bohdan Rusyn

^2,3

,

Oleksandr Melnychenko

¹

,

Tomasz Perzynski

³

,

Anatoliy Sachenko

^3,4

,

Serhii Svystun

¹

and

Oleg Savenko

¹

Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine

²

Department of Information Technologies of Remote Sensing, Karpenko Physico-Mechanical Institute of NAS of Ukraine, 79601 Lviv, Ukraine

³

Faculty of Transport, Electrical Engineering and Computer Science, Casimir Pulaski Radom University, 29, Malczewskiego St., 26-600 Radom, Poland

⁴

Research Institute for Intelligent Computer Systems, West Ukrainian National University, 11, Lvivska Str., 46009 Ternopil, Ukraine

^*

Author to whom correspondence should be addressed.

Energies 2025, 18(17), 4523; https://doi.org/10.3390/en18174523

Submission received: 27 July 2025 / Revised: 18 August 2025 / Accepted: 21 August 2025 / Published: 26 August 2025

(This article belongs to the Special Issue Optimal Control of Wind and Wave Energy Converters)

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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

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MDPI and ACS Style

Radiuk, P.; Rusyn, B.; Melnychenko, O.; Perzynski, T.; Sachenko, A.; Svystun, S.; Savenko, O. Criticality Assessment of Wind Turbine Defects via Multispectral UAV Fusion and Fuzzy Logic. Energies 2025, 18, 4523. https://doi.org/10.3390/en18174523

AMA Style

Radiuk P, Rusyn B, Melnychenko O, Perzynski T, Sachenko A, Svystun S, Savenko O. Criticality Assessment of Wind Turbine Defects via Multispectral UAV Fusion and Fuzzy Logic. Energies. 2025; 18(17):4523. https://doi.org/10.3390/en18174523

Chicago/Turabian Style

Radiuk, Pavlo, Bohdan Rusyn, Oleksandr Melnychenko, Tomasz Perzynski, Anatoliy Sachenko, Serhii Svystun, and Oleg Savenko. 2025. "Criticality Assessment of Wind Turbine Defects via Multispectral UAV Fusion and Fuzzy Logic" Energies 18, no. 17: 4523. https://doi.org/10.3390/en18174523

APA Style

Radiuk, P., Rusyn, B., Melnychenko, O., Perzynski, T., Sachenko, A., Svystun, S., & Savenko, O. (2025). Criticality Assessment of Wind Turbine Defects via Multispectral UAV Fusion and Fuzzy Logic. Energies, 18(17), 4523. https://doi.org/10.3390/en18174523

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

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