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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Majid Memari & Mohammad Shekaramiz & Mohammad A. S. Masoum & Abdennour C. Seibi, 2024. "Data Fusion and Ensemble Learning for Advanced Anomaly Detection Using Multi-Spectral RGB and Thermal Imaging of Small Wind Turbine Blades," Energies, MDPI, vol. 17(3), pages 1-29, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:673-:d:1330196
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    References listed on IDEAS

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    1. Miao Wang & Yunfeng Zhang & Chao Qin & Peipei Liu & Qiuyue Zhang & Rajesh Kaluri, 2022. "Option Pricing Model Combining Ensemble Learning Methods and Network Learning Structure," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, October.
    2. Bao-Yuan Chen & Yu-Kun Shen & Kun Sun, 2020. "Research on Object Detection Algorithm Based on Multilayer Information Fusion," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, September.
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