Squeeze-and-Excitation Networks and the Improved Informer Model for Bearing Fault Diagnosis

This paper presents a fault diagnosis model for rolling bearings that addresses the challenges of establishing long-sequence correlations and extracting spatial features in deep-learning models. The proposed model combines SENet with an improved Informer model. Initially, local features are extracted using the Conv1d method, and input data is optimized through normalization and embedding techniques. Next, the SE-Conv1d network model is employed to enhance key features while suppressing noise interference adaptively. In the improved Informer model, the ProbSparse self-attention mechanism and self-attention distillation technique efficiently capture global dependencies in long sequences within the rolling bearing dataset, significantly reducing computational complexity and improving accuracy. Finally, experiments on the CWRU and HUST datasets demonstrate that the proposed model achieves accuracy rates of 99.78% and 99.45%, respectively. The experimental results show that, compared to other deep learning methods, the proposed model offers superior fault diagnosis accuracy, stability, and generalization ability.