Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Mask-MRNet: A deep neural network for wind turbine blade fault detection
https://orcid.org/0000-0003-1594-0858
In this paper, a deep neural network named Mask-MRNet is proposed to detect wind turbine (WT) blade fault based on images taken by unmanned aerial vehicles. Two datasets of the blade image are built for training and optimizing. Based on the proposed network, the blade images can intuitively express the mask, bounding box, and type of fault. In the detection, the network is stacked with Mask R-CNN-512 and MRNet. Optimized Mask R-CNN, Mask R-CNN-512, can significantly reduce inference time when performing large object detection such as WT blade fault. MRNet is proposed to correct the fault mask angle for cropping the low noise fault image from the original image and classify the fault type. Compared with more than 20 classification models based on indices including training and testing accuracy, the f1-score, and detection efficiency, DenseNet-121 was chosen as the classification model for Mask-MRNet. In addition, it is better to choose the classifier according to specific application demands in practical environments. A computational study was performed to further demonstrate that Mask-MRNet can not only achieve the multifunctional WT blade fault detection but also dynamic monitoring during the running of the WT.
Mask-MRNet: A deep neural network for wind turbine blade fault detection
https://orcid.org/0000-0003-1594-0858
In this paper, a deep neural network named Mask-MRNet is proposed to detect wind turbine (WT) blade fault based on images taken by unmanned aerial vehicles. Two datasets of the blade image are built for training and optimizing. Based on the proposed network, the blade images can intuitively express the mask, bounding box, and type of fault. In the detection, the network is stacked with Mask R-CNN-512 and MRNet. Optimized Mask R-CNN, Mask R-CNN-512, can significantly reduce inference time when performing large object detection such as WT blade fault. MRNet is proposed to correct the fault mask angle for cropping the low noise fault image from the original image and classify the fault type. Compared with more than 20 classification models based on indices including training and testing accuracy, the f1-score, and detection efficiency, DenseNet-121 was chosen as the classification model for Mask-MRNet. In addition, it is better to choose the classifier according to specific application demands in practical environments. A computational study was performed to further demonstrate that Mask-MRNet can not only achieve the multifunctional WT blade fault detection but also dynamic monitoring during the running of the WT.
Mask-MRNet: A deep neural network for wind turbine blade fault detection
https://orcid.org/0000-0003-1594-0858
In this paper, a deep neural network named Mask-MRNet is proposed to detect wind turbine (WT) blade fault based on images taken by unmanned aerial vehicles. Two datasets of the blade image are built for training and optimizing. Based on the proposed network, the blade images can intuitively express the mask, bounding box, and type of fault. In the detection, the network is stacked with Mask R-CNN-512 and MRNet. Optimized Mask R-CNN, Mask R-CNN-512, can significantly reduce inference time when performing large object detection such as WT blade fault. MRNet is proposed to correct the fault mask angle for cropping the low noise fault image from the original image and classify the fault type. Compared with more than 20 classification models based on indices including training and testing accuracy, the f1-score, and detection efficiency, DenseNet-121 was chosen as the classification model for Mask-MRNet. In addition, it is better to choose the classifier according to specific application demands in practical environments. A computational study was performed to further demonstrate that Mask-MRNet can not only achieve the multifunctional WT blade fault detection but also dynamic monitoring during the running of the WT.
Mask-MRNet: A deep neural network for wind turbine blade fault detection
Zhang, Chao (Autor:in) / Wen, Chuanbo (Autor:in) / Liu, Jihui (Autor:in)
01.09.2020
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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