Detection of jacket offshore wind turbine structural damage using an 1D-convolutional neural network with a support vector machine layer
Visualitza/Obre
10.1088/1742-6596/2265/3/032088
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/370216
Tipus de documentArticle
Data publicació2022-05-01
EditorInstitute of Physics (IOP)
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
Abstract
Because offshore wind turbines, particularly their foundations, operate in hostile environments, implementing a structural health monitoring system is one of the best ways to monitor their condition, schedule maintenance, and predict possible fatal failures at lower costs. A novel strategy for detecting damage in offshore wind turbine jacket foundations is developed in this work, based on a vibration monitoring methodology that reshapes the data into a multichannel array, with as many channels as correlated sensors with the predicted variable, a 1-D deep convolutional neural network to extract temporal features from the monitored data, and a support vector machine as a final classification layer. The obtained model allows the detection of three types of bar states: healthy bar, cracked bar, and bar with an unlocked bolt.
CitacióTutivén, C.; Vidal, Y. Detection of jacket offshore wind turbine structural damage using an 1D-convolutional neural network with a support vector machine layer. "Journal of physics: conference series", 1 Maig 2022, vol. 2265, núm. 3.
ISSN1742-6588
Versió de l'editorhttps://iopscience.iop.org/article/10.1088/1742-6596/2265/3/032088
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Tutivén_2022_J._Phys. _Conf._Ser._2265_032088.pdf | 1,684Mb | Visualitza/Obre |