A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning
In engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault...
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Editorial Office of Journal of Shanghai Jiao Tong University
2025-05-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-5-561.shtml |
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| author | PAN Meiqi, HE Xing |
| author_facet | PAN Meiqi, HE Xing |
| author_sort | PAN Meiqi, HE Xing |
| collection | DOAJ |
| description | In engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault category. Unlike traditional methods that directly establish mapping between fault samples and fault categories, a zero-shot learning (ZSL) method for wind turbine fault diagnosis based on fault attributes is proposed to enable fault feature migration. A fault attribute matrix is established by describing the attributes of each fault, which is embedded into the fault sample space and fault category space. Then, a fault attribute learner is developed based on convolutional neural network (CNN), and a fault classifier is established based on Euclidean distance, forming the diagnosis process where fault attributes are predicted from fault samples and then classified. Finally, the effectiveness and superiority of the proposed fault diagnosis method are validated by comparing it with other zero-shot learning methods. |
| format | Article |
| id | doaj-art-c2f5cc710b42475aa5cc5f72cd64042b |
| institution | OA Journals |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-c2f5cc710b42475aa5cc5f72cd64042b2025-08-20T02:32:29ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-05-0159556156810.16183/j.cnki.jsjtu.2023.375A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot LearningPAN Meiqi, HE Xing0a. College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, Chinab. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaIn engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault category. Unlike traditional methods that directly establish mapping between fault samples and fault categories, a zero-shot learning (ZSL) method for wind turbine fault diagnosis based on fault attributes is proposed to enable fault feature migration. A fault attribute matrix is established by describing the attributes of each fault, which is embedded into the fault sample space and fault category space. Then, a fault attribute learner is developed based on convolutional neural network (CNN), and a fault classifier is established based on Euclidean distance, forming the diagnosis process where fault attributes are predicted from fault samples and then classified. Finally, the effectiveness and superiority of the proposed fault diagnosis method are validated by comparing it with other zero-shot learning methods.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-5-561.shtmlwind turbine fault diagnosiszero-shot learning (zsl)convolutional neural network (cnn)knowledge-data hybrid driven |
| spellingShingle | PAN Meiqi, HE Xing A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning Shanghai Jiaotong Daxue xuebao wind turbine fault diagnosis zero-shot learning (zsl) convolutional neural network (cnn) knowledge-data hybrid driven |
| title | A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning |
| title_full | A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning |
| title_fullStr | A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning |
| title_full_unstemmed | A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning |
| title_short | A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning |
| title_sort | fault diagnosis method for wind turbines based on zero shot learning |
| topic | wind turbine fault diagnosis zero-shot learning (zsl) convolutional neural network (cnn) knowledge-data hybrid driven |
| url | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-5-561.shtml |
| work_keys_str_mv | AT panmeiqihexing afaultdiagnosismethodforwindturbinesbasedonzeroshotlearning AT panmeiqihexing faultdiagnosismethodforwindturbinesbasedonzeroshotlearning |