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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/4264

    Título
    An SVM-based solution for fault detection in wind turbines
    Autor
    Santos González, PedroAutoridad UBU
    Villa, Luisa F.
    Reñones, Anibal
    Bustillo Iglesias, AndrésAutoridad UBU Orcid
    Maudes Raedo, Jesús M.Autoridad UBU Orcid
    Publicado en
    Sensors, 2015, V. 15, n. 3, p. 4605-7083
    Editorial
    MDPI
    Fecha de publicación
    2015-03
    ISSN
    1424-8220
    DOI
    10.3390/s150305627
    Résumé
    Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.
    Palabras clave
    fault diagnosis
    neural networks
    support vector machines
    wind turbines
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/4264
    Versión del editor
    http://dx.doi.org/10.3390/s150305627
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    Attribution 4.0 International
    Documento(s) sujeto(s) a una licencia Creative Commons Attribution 4.0 International
    Fichier(s) constituant ce document
    Nombre:
    Santos-Sensors_2015.pdf
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    988.8Ko
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