Wind turbine blade structural state evaluation by hybrid object detector relying on deep learning models

Sarkar, Dipu; Gunturi, Sravan Kumar

doi:10.1007/s12652-020-02587-7

Cited by 18 publications

(8 citation statements)

References 31 publications

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“…Xiao et al [25] used an unmanned aircraft to collect images of wind turbine blades, which they combined with an Alexnet classifier to automatically diagnose blade surface damage. Gunturi et al [26] used super-resolution convolutional neural networks to convert blurred images into high-resolution images in combination with the Yolo-v3 neural network for wind turbine blade damage pattern recognition. In addition, Mao et al [27] proposed a cascaded depth network superior to models such as YOLO-v3 for the automatic detection of multiple types of surface damage to wind turbine blades.…”

Section: Visual Inspectionmentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

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Section: Visual Inspectionmentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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“…Long Wang et al [26] proposed a datadriven framework for the automatic detection of wind turbine blade surface cracks by using unmanned aerial vehicles. Dipu Sarkar et al [27] proposed a YOLOv3-based UAV image recognition model for wind turbine blade damage. ASM Shihavuddin et al [28] used the Inception-ResNet-V2 architecture for Faster R-CNN to propose an efficient automatic detection method for wind turbine blade damage.…”

Section: B Wind Turbine Blade Damage Detectionmentioning

confidence: 99%

Efficient and Accurate Damage Detector for Wind Turbine Blade Images

Yao²,

Wang

et al. 2022

IEEE Access

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The damage of wind turbine blades is one of the main problems restricting wind power development. Object detection can identify the damaged regions and diagnose the damage types. To handle the high-resolution wind turbine blade images, this article presents a novel efficient, and accurate damage detector (EADD) for wind turbine blade images. The proposed method adopts Single Shot MultiBox Detector (SSD) as the detection framework and offers an improved ResNet as the backbone. Firstly, the improved ResNet backbone uses dense connection blocks consisting of factorized depth-wise separable bottleneck (FDSB) and feature aggregation module (FAM), which makes the damage detection model more lightweight and has a faster detection speed. Secondly, the bidirectional cross-scale feature pyramid (BiFPN) is introduced into the proposed method to use multi-scale features fully and have more feature expression. In addition, data pre-processing, exponential moving average (EMA) and label smooth methods are utilized to improve the accuracy and robustness of the model. The experimental results on the wind turbine blade damage detection dataset show that our proposed method can achieve the best trade-off between detection accuracy and computation time compared with other competitive methods.INDEX TERMS Wind turbine blade; damage detection; SSD; dense connection; BiFPN.

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“…Images from a non-public dataset provided commercially by EasyInspect ApS as well as from the DTU-Drone inspection images of the wind turbine dataset [14] were used. The images were annotated to detect leading edge erosions, lighting receptors and vortex generator An approach based on YOLO to surface damage detection was presented by Sakar et al [12]. Here the authors compared the performance of YOLOv3, YOLOv2 and Faster R-CNN models on the Nordtank WT dataset [14], along with 300 additional images collected from the Internet.…”

Section: State Of the Artmentioning

confidence: 99%

“…Sakar et al [12] extended the work in [9] by enhancing and labelling the dataset. The authors also tested the performance of YOLOv3 on this dataset reaching an mAP of 96%.…”

Section: Introductionmentioning

confidence: 99%

Drone Footage Wind Turbine Surface Damage Detection

Foster

Best

Gianni

et al. 2022

2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

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In this work a new publicly available dataset of wind turbine surface damage images is presented. Moreover, a comparison between ResNet-101 Faster R-CNN and YOLOv5 for Wind Turbine Surface Damage Detection is analysed and performance of these models on drone footage with active turbines is also discussed. Results show that YOLOv5 outperforms ResNet-101 Faster R-CNN in predicting the bounding box coordinates of the damaged surfaces of the wind turbines. However, unlike YOLOv5, ResNet-101 Faster R-CNN estimates an entire instance of damage as a single prediction.

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Wind turbine blade structural state evaluation by hybrid object detector relying on deep learning models

Cited by 18 publications

References 31 publications

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Efficient and Accurate Damage Detector for Wind Turbine Blade Images

Drone Footage Wind Turbine Surface Damage Detection

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