Noise reduction and fault diagnosis of the acoustic signals of wind turbine rotating bearings under simulated realistic working conditions

Wind turbines operating in harsh environments have a significantly higher risk of rotating bearing equipment failures. Although fault diagnosis can be achieved by collecting and analyzing acoustic signals from the equipment, various background noises in the environment inevitably interfere with the accuracy of equipment detection. Delayed maintenance may lead to further damage and increased maintenance costs. To address this issue, this work proposes a WGU-Net (Wave-GRU-U-Net) signal denoising network to resolve the background noise problem in acoustic signal acquisition for the rotating bearings of wind turbines under simulated realistic working conditions and consequently enable their fault diagnosis. The network is an improvement over the U-shaped structure of the U-Net: it inserts a gated recurrent unit network between the encoder (downsampling and cropping) and the decoder (upsampling and concatenation) to better preserve temporal dependencies in the signal, enabling reconstruction and denoising. Then, the denoised signals are fed into preset SVM and CNN networks to perform fault diagnosis for rotating bearings of wind turbines under simulated realistic working conditions. The results demonstrate that under multi-noise conditions, the signals denoised by WGU-Net exhibited high recognition accuracy, achieving averages of 97.98% with the SVM classifier and 99.3% with the CNN classifier. These results represent improvements of 9.17% and 6.9%, respectively, compared to the accuracy before denoising.