Data Fusion and Ensemble Learning for Advanced Anomaly Detection Using Multi-Spectral RGB and Thermal Imaging of Small Wind Turbine Blades

Majid Memari, Mohammad Shekaramiz (), Mohammad A. S. Masoum and Abdennour C. Seibi
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Majid Memari: Machine Learning and Drone Laboratory, Engineering Department, Utah Valley University, Orem, UT 84058, USA
Mohammad Shekaramiz: Machine Learning and Drone Laboratory, Engineering Department, Utah Valley University, Orem, UT 84058, USA
Mohammad A. S. Masoum: Machine Learning and Drone Laboratory, Engineering Department, Utah Valley University, Orem, UT 84058, USA
Abdennour C. Seibi: Machine Learning and Drone Laboratory, Engineering Department, Utah Valley University, Orem, UT 84058, USA

Energies, 2024, vol. 17, issue 3, 1-29

Abstract: This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection through the synergistic use of thermal and RGB imaging, coupled with advanced deep learning techniques. We curated a unique dataset of 1000 thermal images of healthy and faulty blades using a FLIR C5 Compact Thermal Camera, which is equipped with Multi-Spectral Dynamic Imaging technology for enhanced imaging. This paper focuses on evaluating 35 deep learning classifiers, with a standout ensemble model combining Vision Transformer (ViT) and DenseNet161, achieving a remarkable 100% accuracy on the dataset. This model demonstrates the exceptional potential of deep learning in thermal diagnostic applications, particularly in predictive maintenance within the renewable energy sector. Our findings underscore the synergistic combination of ViT’s global feature analysis and DenseNet161’s dense connectivity, highlighting the importance of controlled environments and sophisticated preprocessing for accurate thermal image capture. This research contributes significantly to the field by providing a comprehensive dataset and demonstrating the efficacy of several deep learning models in ensuring the operational efficiency and reliability of wind turbines.

Keywords: deep learning; RGB imaging; thermal imaging; data fusion; wind turbine blades; fault detection; image classification; ensemble learning; structural integrity; inspection (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: 2024
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