Wind turbine signal fault diagnosis using deep neural networks-inspired model
This work presents a deep neural network-inspired solution to intelligent signal fault diagnosis for wind turbine gearbox systems. A 1D convolution deep neural network architecture is proposed, constructed and validated. The proposed model was constructed of 1D signal for the input layer, ten differ...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| منشور في: |
2023
|
| الوصول للمادة أونلاين: | http://hdl.handle.net/11675/10926 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcat http://www.scopus.com/inward/record.url?scp=85153848068&partnerID=8YFLogxK |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1870679723882840065 |
|---|---|
| author | Rababaah, Aaron |
| author_facet | Rababaah, Aaron |
| author_role | author |
| dc.creator.none.fl_str_mv | Rababaah, Aaron |
| dc.date.none.fl_str_mv | 2023-01-01 2024-02-05T08:32:40Z 2024-02-05T08:32:40Z |
| dc.identifier.none.fl_str_mv | 10.1504/IJCAT.2022.129378 http://hdl.handle.net/11675/10926 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcat http://www.scopus.com/inward/record.url?scp=85153848068&partnerID=8YFLogxK |
| dc.publisher.none.fl_str_mv | Inderscience Enterprises Ltd |
| dc.relation.none.fl_str_mv | Computer Science and Info Systems International Journal of Computer Applications in Technology |
| dc.title.none.fl_str_mv | Wind turbine signal fault diagnosis using deep neural networks-inspired model |
| dc.type.none.fl_str_mv | Exhibition info:eu-repo/semantics/publishedVersion |
| description | This work presents a deep neural network-inspired solution to intelligent signal fault diagnosis for wind turbine gearbox systems. A 1D convolution deep neural network architecture is proposed, constructed and validated. The proposed model was constructed of 1D signal for the input layer, ten different learned kernels as signal features, convolution layer, activation layer using rectified linear unit function, max-pooling layer, flatten layer and traditional multi-perceptron neural network for classification with soft-max class assignment. The data was acquired from real-world experiments conducted on real wind turbine gearboxes and archived by the National Renewable Energy Labs of the US Department of Energy. Ten independent experiments were conducted on 2,400,000 data points and the proposed model produced a mean classification accuracy of 96.14% for normal signals with a standard deviation of 0.0027 and a mean classification accuracy of 99.87% for faulty signals with a standard deviation of 0.0016. |
| id | AUKR_a612017f6111d737f144fbfbfca8d723 |
| identifier_str_mv | 10.1504/IJCAT.2022.129378 |
| network_acronym_str | AUKR |
| network_name_str | AU Kuwait Rep |
| oai_identifier_str | oai:dspace.auk.edu.kw:11675/10926 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | Inderscience Enterprises Ltd |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Wind turbine signal fault diagnosis using deep neural networks-inspired modelRababaah, AaronThis work presents a deep neural network-inspired solution to intelligent signal fault diagnosis for wind turbine gearbox systems. A 1D convolution deep neural network architecture is proposed, constructed and validated. The proposed model was constructed of 1D signal for the input layer, ten different learned kernels as signal features, convolution layer, activation layer using rectified linear unit function, max-pooling layer, flatten layer and traditional multi-perceptron neural network for classification with soft-max class assignment. The data was acquired from real-world experiments conducted on real wind turbine gearboxes and archived by the National Renewable Energy Labs of the US Department of Energy. Ten independent experiments were conducted on 2,400,000 data points and the proposed model produced a mean classification accuracy of 96.14% for normal signals with a standard deviation of 0.0027 and a mean classification accuracy of 99.87% for faulty signals with a standard deviation of 0.0016.Inderscience Enterprises Ltd2024-02-05T08:32:40Z2024-02-05T08:32:40Z2023-01-01Exhibitioninfo:eu-repo/semantics/publishedVersion10.1504/IJCAT.2022.129378http://hdl.handle.net/11675/10926https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcathttp://www.scopus.com/inward/record.url?scp=85153848068&partnerID=8YFLogxKComputer Science and Info SystemsInternational Journal of Computer Applications in Technologyoai:dspace.auk.edu.kw:11675/109262025-06-18T09:03:51Z |
| spellingShingle | Wind turbine signal fault diagnosis using deep neural networks-inspired model Rababaah, Aaron |
| status_str | publishedVersion |
| title | Wind turbine signal fault diagnosis using deep neural networks-inspired model |
| title_full | Wind turbine signal fault diagnosis using deep neural networks-inspired model |
| title_fullStr | Wind turbine signal fault diagnosis using deep neural networks-inspired model |
| title_full_unstemmed | Wind turbine signal fault diagnosis using deep neural networks-inspired model |
| title_short | Wind turbine signal fault diagnosis using deep neural networks-inspired model |
| title_sort | Wind turbine signal fault diagnosis using deep neural networks-inspired model |
| url | http://hdl.handle.net/11675/10926 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcat http://www.scopus.com/inward/record.url?scp=85153848068&partnerID=8YFLogxK |