Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM
Aiming at the safety hidden danger caused by blade faults that are difficult to be detected, the fault prevention detection technology based on blade image intelligent processing is investigated. A wind turbine blade fault prevention detection method is designed to analyses wind turbine blade images...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-05-01
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| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202512-28-12-0012 |
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| _version_ | 1849714398880333824 |
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| author | Dianming WANG Xue PAN Jian MA Yuzhang DAI Chengjun SUN Shibin LI Xiaoju YIN |
| author_facet | Dianming WANG Xue PAN Jian MA Yuzhang DAI Chengjun SUN Shibin LI Xiaoju YIN |
| author_sort | Dianming WANG |
| collection | DOAJ |
| description | Aiming at the safety hidden danger caused by blade faults that are difficult to be detected, the fault prevention detection technology based on blade image intelligent processing is investigated. A wind turbine blade fault prevention detection method is designed to analyses wind turbine blade images by combining DenseNet,
Transfer Learning (TL) and Extreme Learning Machines (ELM), and collect image samples as a training set. The image samples are collected as training set, and the image features are effectively extracted using the improved DenseNet, which is combined with Extreme Learning Machines to improve the classification accuracy of the detection. 8000 images were collected, and the analysis results for the test set of images show that the detection accuracy of this model is higher than that of the DenseNet, ResNet and AlexNet models of migration learning, reaching more than 99%, and obtaining a more accurate preventive detection of wind turbine blade faults. |
| format | Article |
| id | doaj-art-e792ea2c8b1d482295ce33207c4e7d0b |
| institution | DOAJ |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-e792ea2c8b1d482295ce33207c4e7d0b2025-08-20T03:13:42ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-05-0128122451246010.6180/jase.202512_28(12).0012Research on wind turbine blade fault detection based on DenseNet-TL combined with ELMDianming WANG0Xue PAN1Jian MA2Yuzhang DAI3Chengjun SUN4Shibin LI5Xiaoju YIN6School of Renewable Energy, Shenyang Institute of Engineering, Shenyang 110136, Liaoning Province, ChinaSchool of Renewable Energy, Shenyang Institute of Engineering, Shenyang 110136, Liaoning Province, ChinaHuaneng International Power Company Limited Dandong Power Plant, Donggang 110167, Liaoning Province, ChinaHuaneng International Power Company Limited Dandong Power Plant, Donggang 110167, Liaoning Province, ChinaHuaneng International Power Company Limited Dandong Power Plant, Donggang 110167, Liaoning Province, ChinaHuaneng International Power Company Limited Dandong Power Plant, Donggang 110167, Liaoning Province, ChinaSchool of Renewable Energy, Shenyang Institute of Engineering, Shenyang 110136, Liaoning Province, ChinaAiming at the safety hidden danger caused by blade faults that are difficult to be detected, the fault prevention detection technology based on blade image intelligent processing is investigated. A wind turbine blade fault prevention detection method is designed to analyses wind turbine blade images by combining DenseNet, Transfer Learning (TL) and Extreme Learning Machines (ELM), and collect image samples as a training set. The image samples are collected as training set, and the image features are effectively extracted using the improved DenseNet, which is combined with Extreme Learning Machines to improve the classification accuracy of the detection. 8000 images were collected, and the analysis results for the test set of images show that the detection accuracy of this model is higher than that of the DenseNet, ResNet and AlexNet models of migration learning, reaching more than 99%, and obtaining a more accurate preventive detection of wind turbine blade faults.http://jase.tku.edu.tw/articles/jase-202512-28-12-0012wind turbine bladeextreme learning machinetransfer learningimage recognitiondensenet |
| spellingShingle | Dianming WANG Xue PAN Jian MA Yuzhang DAI Chengjun SUN Shibin LI Xiaoju YIN Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM Journal of Applied Science and Engineering wind turbine blade extreme learning machine transfer learning image recognition densenet |
| title | Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM |
| title_full | Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM |
| title_fullStr | Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM |
| title_full_unstemmed | Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM |
| title_short | Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM |
| title_sort | research on wind turbine blade fault detection based on densenet tl combined with elm |
| topic | wind turbine blade extreme learning machine transfer learning image recognition densenet |
| url | http://jase.tku.edu.tw/articles/jase-202512-28-12-0012 |
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