Bearing Fault Diagnosis Method Based on SE-CNN

As a key component in rotating machinery, the health condition of rolling bearings directly affects the operational efficiency and safety of the equipment. Traditional bearing fault diagnosis methods rely on signal processing techniques and empirical feature extraction, but in practical applications, with the change of working conditions, the signal features often appear to be mixed, which brings a greater challenge to the diagnosis. In this paper, a bearing fault diagnosis method based on Squeeze-and-Excitation Convolutional Neural Network is proposed. The method enhances the network's focus on key fault features by introducing the SE module to adaptively adjust the weights of each feature channel in the convolutional network, which optimizes the feature extraction ability of the model in complex vibration signals. The experimental results show that the SE-CNN performs superiorly in terms of precision rate, recall rate and F1-score, especially in the case of category imbalance, the SE-CNN has better robustness.