A bearing fault diagnosis model with enhanced feature extraction based on the Kolmogorov-Arnold representation Theorem and an attention mechanism

The stability and reliability of bearing operation are essential for safe production. Currently, convolutional neural networks (CNNs) are widely used in bearing fault diagnosis. However, traditional convolutional kernels are structurally and parametrically limited, making it difficult to extract complex and subtle fault features. Additionally, conventional CNNs often fail to distinguish the relative importance of features, leading to missed critical information and reduced diagnostic performance. To address these challenges, this paper proposes a bearing fault diagnosis model based on a convolutional network with enhanced feature extraction capability: Kolmogorov Arnold Convolutional Squeeze-and-Excitation Network (KACSEN). The model leverages convolution kernels defined by nonlinear functions with learnable parameters to better capture intricate feature patterns. Additionally, the integrated attention mechanism dynamically reweights feature channels, enhancing the model's sensitivity to fault features. Experiments conducted on the NEFU bearing dataset and the CWRU bearing dataset achieved accuracy rates of 99.38% and 99.27%, respectively. This study enables accurate identification of bearing faults, which holds significant importance for ensuring production safety.