A motor bearing fault voiceprint recognition method based on Mel-CNN model

The occurrence of bearing faults is often accompanied by noise signals, and noise sensors have the characteristics of non-contact and flexible arrangement; hence, this paper proposes a bearing fault diagnosis method based on voiceprint features and deep learning. First, the high-frequency component of the motor noise is removed with the help of variational mode decomposition (VMD) to extract the Mel spectrum voiceprint features. Secondly, the Mel voiceprint features are re-extracted with the help of convolutional neural networks (CNN) to fully obtain the high-dimensional abstract features characterizing the bearing faults. Finally, the Mel-CNN model is exploited to achieve bearing fault diagnosis. Applying the Mel-CNN model proposed in this paper to motor noise data with bearing faults, the results show that the Mel spectral features can accurately characterize bearing faults and that the Mel-CNN model outperforms ACDIN, WDCNN, TICNN, the improved LeNet-5 model, and four CNN-derived models.