Incorporating Machine Learning into Vibration Detection for Wind Turbines
With machine learning techniques, wind turbine components can be detected and diagnosed in advance, so degeneration can be prevented. Automatic and autonomous learning is used to predict, detect, and diagnose electrical and mechanical failures in wind turbines. Based on the implementation of machine...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/6572298 |
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| _version_ | 1832548618161094656 |
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| author | J. Vives |
| author_facet | J. Vives |
| author_sort | J. Vives |
| collection | DOAJ |
| description | With machine learning techniques, wind turbine components can be detected and diagnosed in advance, so degeneration can be prevented. Automatic and autonomous learning is used to predict, detect, and diagnose electrical and mechanical failures in wind turbines. Based on the implementation of machine learning algorithms adapted to the different components and faults of wind turbines, this study evaluates different methodologies for monitoring, supervision, and fault diagnosis. |
| format | Article |
| id | doaj-art-89044f01d8cd4744856c17dc3c4c5736 |
| institution | Kabale University |
| issn | 1687-5605 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-89044f01d8cd4744856c17dc3c4c57362025-02-03T06:13:30ZengWileyModelling and Simulation in Engineering1687-56052022-01-01202210.1155/2022/6572298Incorporating Machine Learning into Vibration Detection for Wind TurbinesJ. Vives0Institute of Automatic and Industrial InformaticsWith machine learning techniques, wind turbine components can be detected and diagnosed in advance, so degeneration can be prevented. Automatic and autonomous learning is used to predict, detect, and diagnose electrical and mechanical failures in wind turbines. Based on the implementation of machine learning algorithms adapted to the different components and faults of wind turbines, this study evaluates different methodologies for monitoring, supervision, and fault diagnosis.http://dx.doi.org/10.1155/2022/6572298 |
| spellingShingle | J. Vives Incorporating Machine Learning into Vibration Detection for Wind Turbines Modelling and Simulation in Engineering |
| title | Incorporating Machine Learning into Vibration Detection for Wind Turbines |
| title_full | Incorporating Machine Learning into Vibration Detection for Wind Turbines |
| title_fullStr | Incorporating Machine Learning into Vibration Detection for Wind Turbines |
| title_full_unstemmed | Incorporating Machine Learning into Vibration Detection for Wind Turbines |
| title_short | Incorporating Machine Learning into Vibration Detection for Wind Turbines |
| title_sort | incorporating machine learning into vibration detection for wind turbines |
| url | http://dx.doi.org/10.1155/2022/6572298 |
| work_keys_str_mv | AT jvives incorporatingmachinelearningintovibrationdetectionforwindturbines |