Diagnosis of Sliding Bearing Lubrication State Based on SC-ResNet

In response to the issue of poor diagnostic performance of models due to the scarcity of samples for lubrication failure states in sliding bearings in practical engineering applications, this thesis proposes a fault diagnosis method for the lubrication state of sliding bearings based on a Self-Calibrated Residual Network (SC-ResNet). By leveraging the self-calibrated convolution, which significantly enhances the network's receptive field and feature extraction capabilities without increasing the number of parameters and complexity, a self-calibrated residual block is designed to construct the SC-ResNet model. This model is capable of diagnosing the lubrication state of bearings using a small number of samples inputted into a pre-trained model. Experimental results indicate that this method performs exceptionally well under conditions with a limited number of samples, achieving higher Recall and F1-score values compared to other methods.