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Unsupervised domain adaptation (UDA) has shown remarkable results in bearingfault diagnosis under changing working conditions in recent years. However,most UDA methods do not consider the geometric structure of the data.Furthermore, the global domain adaptation technique is commonly applied, whichignores the relation between subdomains. This paper addresses mentionedchallenges by presenting the novel deep subdomain adaptation graph convolutionneural network (DSAGCN), which has two key characteristics: First, graphconvolution neural network (GCNN) is employed to model the structure of data.Second, adversarial domain adaptation and local maximum mean discrepancy (LMMD)methods are applied concurrently to align the subdomain's distribution andreduce structure discrepancy between relevant subdomains and global domains.CWRU and Paderborn bearing datasets are used to validate the DSAGCN method'sefficiency and superiority between comparison models. The experimental resultsdemonstrate the significance of aligning structured subdomains along withdomain adaptation methods to obtain an accurate data-driven model inunsupervised fault diagnosis.

Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosis