AI techniques applied to diagnosis of vibrations failures in wind turbines
Supervision and fault diagnosis in wind turbines using automatic learning techniques allow early detection of the degeneration of the components, as well as the detection and diagnosis of sudden failures. This contribution evaluates different machine learning methodologies to predict, detect and dia...
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| Veröffentlicht in: | Revista IEEE América Latina 2020-08, Vol.18 (8), p.1478-1486 |
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| container_title | Revista IEEE América Latina |
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| creator | Vives, Javier Quiles, Eduardo Garcia, Emilio |
| description | Supervision and fault diagnosis in wind turbines using automatic learning techniques allow early detection of the degeneration of the components, as well as the detection and diagnosis of sudden failures. This contribution evaluates different machine learning methodologies to predict, detect and diagnose electrical and mechanical failures of wind turbines. An integrated monitoring and diagnostic system is proposed using automatic learning algorithms adapted to the different components and faults of the wind turbine |
| doi_str_mv | 10.1109/TLA.2020.9111685 |
| format | Article |
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| ispartof | Revista IEEE América Latina, 2020-08, Vol.18 (8), p.1478-1486 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms condition monitoring Deep learning Degeneration Diagnostic systems Failure fault detection Fault diagnosis Irrigation Machine learning Monitoring Silicon compounds Support vector machines Wind turbines |
| title | AI techniques applied to diagnosis of vibrations failures in wind turbines |
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