Abstract: Accurate and interpretable fault diagnosis of wind turbines (WTs) is critical for ensuring reliable and efficient operation. However, existing model-driven and data-driven approaches often ...
Abstract: Here, we propose a hybrid Deep Learning (DL) framework consisting of a Denoising Autoencoder (DAE), Convolutional Neural Network (CNN), Bidirectional LSTM (BiLSTM), and a custom Attention ...
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