Cross-domain fault diagnosis of rotating machinery based on graph feature extraction

Abstract

Transfer learning can realize cross-domain fault diagnosis of rotating machinery, where the model trained on many labeled samples collected in one working condition can be transferred to insufficient samples collected in the target working condition. Currently, the data features cannot be completely extracted by existing methods when the data distribution gap of the samples collected in different working conditions is quite large. In order to fully extract the data features of rotating machinery to achieve cross-domain fault diagnosis, this paper investigated a cross-domain fault diagnosis model of rotating machinery based on graph feature extraction. The proposed method can realize unsupervised fault diagnosis on rotating machinery running under different working conditions by extracting the numerical and structural features of source and target domains. First of all, data features with large data distribution gaps need to be fully extracted, so a convolutional network based on a deformable convolutional network (De-conv) is designed to extract the features with large differences in data distribution under various working conditions. Secondly, features are extracted based on a convolutional neural network for data values in existing domain adaptation (DA) methods while the structure features of machine monitoring data are ignored. Therefore, a composite spectral-based graph convolutional network is designed to extract structural features of data. Thirdly, fully extracted features are input into a universal DA network to achieve cross-domain fault diagnosis of unknown faults in rotating machinery under changing working conditions. Finally, a benchmarking data set and a data set collected from a practical experimental platform are used to verify the effectiveness of the proposed model, and the results show that it is more suitable for cross-domain fault diagnosis of rotating machinery than other comparison models.