Domain generalization combining covariance loss with graph convolutional networks for intelligent fault diagnosis of rolling bearings

Intelligent fault diagnosis of rolling bearings has advanced significantly with the increase in labeled industrial data. However, the limited data for unknown working conditions poses a challenge to the generalization capabilities of current deep learning methods. Therefore, this article proposes a novel approach to domain generalization, leveraging a combination of covariance loss and graph convolutional networks to realize feature augmentation for intelligent fault diagnosis. This method employs random receptive field layers in feature extractors to project inputs from each source domain into distinct feature spaces. Moreover, a covariance loss is incorporated to ensure the dissimilarity of feature representations. Consequently, the augmented features contribute to the construction of an expanded adjacency matrix and prototypes within graph convolutional networks, thereby enhancing the model's capacity to generalize to unknown domains. Results on both a public dataset and an experimental dataset of rolling bearings have shown the superiority of the proposed approach.