2022
DOI: 10.29354/diag/151608
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Recurrent neural network optimization for wind turbine condition prognosis

Abstract: This research focuses on employing Recurrent Neural Networks (RNN) to prognosis a wind turbine operation's health from collected vibration time series data, by using several memory cell variations, including Long Short Time Memory (LSTM), Bilateral LSTM (BiLSTM), and Gated Recurrent Unit (GRU), which are integrated into various architectures. We tune the training hyperparameters as well as the adapted depth and recurrent cell number of the proposed networks to obtain the most accurate predictions. Tuning those… Show more

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Cited by 3 publications
(1 citation statement)
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“…This method demonstrates the effectiveness of this predictive approach. Adlen et al [24] conducted research using LSTM, Bi-LSTM, and GRU to predict the condition of wind turbine operations based on vibration time series data. In this study, Bayesian optimization was used to fine-tune the training parameters.…”
Section: Related Workmentioning
confidence: 99%
“…This method demonstrates the effectiveness of this predictive approach. Adlen et al [24] conducted research using LSTM, Bi-LSTM, and GRU to predict the condition of wind turbine operations based on vibration time series data. In this study, Bayesian optimization was used to fine-tune the training parameters.…”
Section: Related Workmentioning
confidence: 99%