An AI-Driven Approach to Wind Turbine Bearing Fault Diagnosis from
  Acoustic Signals
Zhao Wang Xiaomeng Li Na Li Longlong Shu
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 demonstrated excellent
generalization ability during validation, indicating its proficiency of
generalization capability. On the test samples, the model achieved remarkable
classification performance, with an overall accuracy exceeding 99.5%, and a
false positive rate of less than 1% for normal status. The findings of this
study provide essential support for the diagnosis and maintenance of bearing
faults in wind turbine generators, with the potential to enhance the
reliability and efficiency of wind power generation.