An AI-Driven Approach to Wind Turbine Bearing Fault Diagnosis from Acoustic Signals

arXiv:2403.09030v1 Announce Type: cross
Abstract: This study aimed to develop a deep learning model for the classification of bearing faults in wind turbine generators from acoustic signals. A convolutional LSTM model was successfully constructed and trained by using audio data from five predefined fault types for both training and validation. To create the dataset, raw audio signal data was collected and processed in frames to capture time and frequency domain information. The model exhibited outstanding accuracy on training samples and …

abstract arxiv audio classification cs.lg cs.sd data deep learning diagnosis eess.as five generators lstm lstm model study training type types wind