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• W4387048596 abstract "ABSTRACTOffshore wind turbines (OWTs) have complex operating conditions and are prone to accidents. Timely and accurate vibration prediction can reduce the probability of failure of OWTs. To address this issue, this study proposed a vibration prediction model based on long short-term memory (LSTM) network. The proposed prediction model was applied to an OWT in an offshore wind farm in China, and three evaluation indicators, i.e. mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE), were used to verify the vibration prediction performance of LSTM. The results demonstrated that the prediction accuracy of LSTM was better than that of back propagation (BP) neural network, and LSTM had optimum engineering practicability. The prediction accuracy of LSTM and BP neural networks decreased with an increase in prediction durations. However, the prediction accuracy of LSTM was higher. This study improves the online safety monitoring of OWTs.KEYWORDS: Offshore wind turbinevibration predictionlong short-term memory networksafety monitoringsuction bucket foundation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Foundation for Innovative Research Groups of the Natural Science Foundation of Hebei Province: [grant number E2020402074]; Southern Offshore Wind Power Joint Development Co., Ltd.: [grant number FDGC20200301GR02]; Tianjin Natural Science Foundation: [grant number 20JCQNJC01540]; the Science Fund for Creative Research Groups of the National Natural Science Foundation of China: [grant number 51621092]; the Program of Introducing Talents of Discipline to Universities: [grant number B14012]." @default.
• W4387048596 created "2023-09-27" @default.
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• W4387048596 date "2023-09-26" @default.
• W4387048596 modified "2023-09-27" @default.
• W4387048596 title "Vibration prediction of offshore wind turbines based on long short-term memory network" @default.
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• W4387048596 doi "https://doi.org/10.1080/17445302.2023.2260941" @default.
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