Intelligent rolling bearing imbalanced fault diagnosis based on Mel-Frequency Cepstrum Coefficient and Convolutional Neural Networks

To improve the bearing fault diagnosis performance under the condition of data distribution shift, an intelligent diagnosis method based on MFCC (Mel-Frequency Cepstrum Coefficient) and MECNN (Convolutional Neural Networks optimized by Mode Normalization (MN) and Efficient Channel Attention (ECA)) is proposed. Firstly, Mel filters are adopted to extract the feature of different frequency bands of vibration signal, and by the feature enhancement of Cepstrum Lifting Technique, the final 2D MFCC is obtained. Secondly, MN is applied to reduce the internal covariant shift caused by the data distribution discrepancy, and improve the generalization ability. ECA is adopted to enhance the fault feature and improve anti-interference ability. Finally, experiments under data distribution shift have been carried out, and an average accuracy of 99.72% was obtained under the data imbalance, and 99.50% was obtained under the operating condition change. Compared with the existing methods, the proposed has higher accuracy and better domain adaptability.