Efficient and Accurate Damage Detector for Wind Turbine Blade Images

Lv, Liang; Yao, Zhongyuan; Wang, Enming; Ren, Xin; Pang, Ruoming; Wang, Hua; Wu, Hao

doi:10.1109/access.2022.3224446

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…In the realm of wind turbine inspections, deep learning has been particularly transformative. For example, Wu developed an efficient and accurate damage detector for WTB images [25]. Zhang et al explored image recognition of WTB defects using attention-based mobilenetv1-yolov4 and transfer learning [26], while Peng et al worked on motion blur removal for drone-based WTB images using synthetic datasets [27].…”

Section: Deep Learning In Wtb Defect Detectionmentioning

confidence: 99%

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

Memari,

Shekaramiz,

Masoum

et al. 2024

Energies

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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.

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Section: Deep Learning In Wtb Defect Detectionmentioning

confidence: 99%

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

Memari,

Shekaramiz,

Masoum

et al. 2024

Energies

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“…Lv et al [135] introduced a novel method for efficient and precise WTB damage detection using the Single Shot MultiBox Detector (SSD) framework with an enhanced ResNet backbone. This method leverages dense connection blocks, a bidirectional cross-scale feature pyramid, and various techniques such as data pre-processing, exponential moving average, and label smoothing to optimize damage detection.…”

Section: Visual Inspection Using Rgb Camerasmentioning

confidence: 99%

“…FIGURE 32: The damage detection results of the proposed method, where red boxes represent gelcoat peeling off, green boxes represent surface cracking, black boxes represent surface corrosion[135].…”

mentioning

confidence: 99%

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Memari,

Shakya,

Shekaramiz

et al. 2024

IEEE Access

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The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines. As WTBs are crucial and costly components, they may suffer material degradation and fatigue failure, which affects their performance and safety. Thus, the urgency for efficient and regular monitoring to maintain their structural integrity is greater than ever. This review paper explores innovative methods in fatigue testing, damage detection, and structural reliability in WTBs, focusing on the use of recent inspection methods, including those that take advantage of drones. Drones are used to identify defects such as cracks, erosion, and coating irregularities using high-resolution imagery with the onboard cameras. Various investigators have developed novel data-driven approaches, incorporating machine learning and deep learning, to accurately identify these defects. Although deep learning-based image processing has been successful in other public infrastructure contexts, its application to wind turbine inspection from aerial images presents unique challenges. This paper also highlights the critical role of failure inspection in enhancing the operational integrity of WTBs, showcasing state-of-the-art deep learning techniques that are pivotal for identifying and analyzing failures in WTBs from images captured by drones. The paper provides insights into the latest developments in using drone imagery for blade defect detection, contrasting this method with traditional non-destructive techniques. This approach could significantly transform the wind energy industry by offering a more efficient, automated, and precise way of ensuring the structural health of wind turbines. Unlike previous studies that predominantly focus on isolated aspects such as inspection or fatigue, this review paper not only integrates the three major aspects of WTBs integrity in terms of aerial inspection, image processing using machine learning, and structural integrity of the blade but also undertakes an extensive examination of the prevailing methodologies in the field, pinpointing crucial gaps and challenges. It provides a detailed review of existing research, covering various areas including automated inspection, image processing techniques, fatigue analysis, and the reliability of wind turbines. This approach enriches the discourse by offering a multifaceted perspective on WTB maintenance, thereby advancing the understanding of operational integrity within the field of wind energy.

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“…As a result, the current research focus on these models is primarily aimed at improving their detection accuracy. For instance, Lv et al [16] developed the EADD detector, which utilized an improved SSD framework and ResNet backbone network, incorporating FSDB and FAM technologies for fast and accurate blade damage detection. Zhu et al [17] significantly improved network performance by replacing the backbone VGG of the SSD one-stage object detection model with ResNet and ResNeXt, without increasing the complexity of the network structure.…”

Section: Introductionmentioning

confidence: 99%

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

Tong,

Fan,

Peng

et al. 2024

Sustainability

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Wind turbine blades are the core components responsible for efficient wind energy conversion and ensuring stability. To address challenges in wind turbine blade damage detection using image processing techniques such as complex image backgrounds, decreased detection performance due to high image resolution, prolonged inference time, and insufficient recognition accuracy, this study introduces an enhanced wind turbine blade damage detection model named WTDB-YOLOv8. Firstly, by incorporating the GhostCBS and DFSB-C2f modules, the aim is to reduce the number of model parameters while enhancing feature extraction capability. Secondly, by integrating the MHSA-C2f module, which incorporates a multi-head self-attention mechanism, the focus on global information is enabled, thereby mitigating irrelevant background interference and reducing the impact of complex backgrounds on damage detection. Lastly, adopting the Mini-BiFPN structure improves the retention of features for small target objects in shallow networks and reinforces the propagation of these features in deep networks, thereby enhancing the detection accuracy of small target damage and reducing false negative rates. Through training and testing on the Wind Turbine Blade Damage Dataset (WTBDD), the WTDB-YOLOv8 model achieves an average precision of 98.3%, representing a 2.2 percentage point improvement over the original YOLOv8 model. Particularly noteworthy is the increase in precision from 93.1% to 97.9% in small target damage detection. Moreover, the total parameter count of the model decreases from 3.22 million in YOLOv8 to 1.99 million, marking a reduction of 38.2%. Therefore, the WTDB-YOLOv8 model not only enhances the detection performance and efficiency of wind turbine blade damage but also significantly reduces the model parameter count, showcasing its practical advantages in engineering applications.

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Efficient and Accurate Damage Detector for Wind Turbine Blade Images

Cited by 12 publications

References 40 publications

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

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

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

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