Residual and Wavelet based Neural Network for the Fault Detection of Wind Turbine Blades

N'Diaye, Lalle M.; Phillips, Austin; A.S., Masoum Mohammad; Shekaramiz, Mohammad

doi:10.1109/ietc54973.2022.9796852

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…To augment the robustness of the dataset, the captured images will be systematically shared with the image processing and deep learning team at Utah Valley University. This collaborative endeavor will facilitate the utilization of their prior research findings [44,45], providing a foundation for training deep convolutional neural networks (CNNs). The primary objective of this collaborative effort is to refine the models, enabling them to discriminate between damaged and healthy blades with a heightened level of accuracy and reliability.…”

Section: Future Workmentioning

confidence: 99%

Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation

Rodriguez,

Shekaramiz,

Masoum

2024

Drones

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The application of computer vision in conjunction with GPS is essential for autonomous wind turbine inspection, particularly when the drone navigates through a wind farm to detect the turbine of interest. Although drones for such inspections use GPS, our study only focuses on the computer vision aspect of navigation that can be combined with GPS information for better navigation in a wind farm. Here, we employ an affordable, non-GPS-equipped drone within an indoor setting to serve educational needs, enhancing its accessibility. To address navigation without GPS, our solution leverages visual data captured by the drone’s front-facing and bottom-facing cameras. We utilize Hough transform, object detection, and QR codes to control drone positioning and calibration. This approach facilitates accurate navigation in a traveling salesman experiment, where the drone visits each wind turbine and returns to a designated launching point without relying on GPS. To perform experiments and investigate the performance of the proposed computer vision technique, the DJI Tello EDU drone and pedestal fans are used to represent commercial drones and wind turbines, respectively. Our detailed and timely experiments demonstrate the effectiveness of computer vision-based path planning in guiding the drone through a small-scale surrogate wind farm, ensuring energy-efficient paths, collision avoidance, and real-time adaptability. Although our efforts do not replicate the actual scenario of wind turbine inspection using drone technology, they provide valuable educational contributions for those willing to work in this area and educational institutions who are seeking to integrate projects like this into their courses, such as autonomous systems.

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Section: Future Workmentioning

confidence: 99%

Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation

Rodriguez,

Shekaramiz,

Masoum

2024

Drones

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“…Organizations such as Clobotics and Arthwind are pioneering work in this field [12,13]. Similar interest is seen in academia [14][15][16][17]. One issue that still needs to be solved with the adoption of drone inspection is presenting the data acquired from drones in a way that is understandable to the engineers performing maintenance [11].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Ward,

Miller,

Engel

et al. 2024

Machines

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Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210×210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University. The images were used to describe in detail the analysis and implementation of the VGG19, Xception, and SVM algorithms using different optimization, model training, and hyperparameter tuning technologies. The performances of these three algorithms are compared in depth alongside those augmented using the proposed FCE image preprocessing technique.

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“…Previous approaches to blade fault detection include the use of residual and wavelet-based neural networks, feature detection using Haar-like features, SVM with fuzzy logic, etc. [37][38][39]. In China, an alternate approach to the problem was recently employed via Unmanned Aerial Vehicles (UAVs) to first capture images of wind turbine blades and then use a cascading classifier algorithm to identify and locate wind turbine blade cracks [34,40].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

Altice,

Nazario,

Davis

et al. 2024

Energies

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Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification.

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Residual and Wavelet based Neural Network for the Fault Detection of Wind Turbine Blades

Cited by 11 publications

References 6 publications

Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation

Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

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