Interpretable parallel channel encoding convolutional neural network for bearing fault diagnosis

Interpretability plays a crucial role in the application of neural networks for fault diagnosis. Integrating preprocessing methods into neural networks can enhance interpretability while preserving their 'end-to-end' characteristics. However, when only redesigning the first layer, subsequent structures still exhibit limited transparency. Additionally, traditional convolution structure is ill-suited for analyzing the readable feature maps derived from vibration signals. To address these challenges, this paper proposes a novel convolution structure for the parameterized signal processing function of the first-layer convolution kernel. This structure incorporates channel mixing for feature augmentation, designs a condensed feature encoder for aggregating and compressing features channel-by-channel, ensures the interpretability of feature map processing, and obtains condensed feature codes to propose smooth activation layer-wise relevance propagation (SA-LRP) method that to perform interpretability analysis. Additionally, cubic feature screening is implemented for diagnostic classification to improve structural fitness. We design experiments using multiple datasets to test various indicators of the structure. The results confirm that connecting our convolution architecture for subsequent analysis outperforms other convolution architectures for the convolution kernel of the first-layer parameterized signal processing function. The interpretability of the model is evaluated through SA-LRP method and validates the interpretability of the model.