This study focuses on developing a convolutional LSTM model to classify bearing faults in wind turbine generators based on acoustic signals. The model achieved high accuracy during training and validation, showcasing exceptional generalization capabilities. By collecting raw audio signal data and processing it into frames, the model demonstrated remarkable performance in classifying various fault types with an overall accuracy exceeding 99.5%. The findings suggest that this AI-driven approach can significantly enhance the diagnosis and maintenance of bearing faults in wind turbines, potentially improving the reliability and efficiency of wind power generation. The research highlights the importance of leveraging artificial intelligence techniques to automate fault diagnosis processes efficiently.
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