A novel intelligent fault diagnosis method of rotating machinery based on signal-to-image mapping and deep Gabor convolutional adaptive pooling network

To address the limitations of insufficient feature representation and easy to be overwhelmed by strong noise in the grayscale images of vibration signals, the random generation and single structure of convolutional kernels of the convolutional neural network leading to insufficient extracted features, and the max pooling and average pooling leading to model overfitting and suppression of critical fault features, this paper proposes a novel diagnosis method based on signal-to-image mapping and deep Gabor convolutional adaptive pooling network. Firstly, this paper designs a vibration signal-to-image mapping strategy to highlight the fault information of vibration signal. Then Gabor convolutional filter is proposed instead of convolutional kernels to guide the model to extract multi-scale and multi-directional fault features. Next, the dynamic adaptive pooling is designed to facilitate the retention of local features and suppress the decay of critical features. Finally, a deep Gabor convolutional adaptive pooling network model is constructed to improve the robustness of the fault feature extraction process and the generalization of the model. The results of the bearing and gear datasets indicate that the proposed method enhances the feature extraction ability, improves the robustness and generalization of the model, and realizes highly accurate fault diagnosis.

Automated summary

The paper proposes a novel diagnosis method based on signal-to-image mapping and deep Gabor convolutional adaptive pooling network, improving the feature extraction and model generalization in vibration signal fault diagnosis.