  Unsupervised domain adaptation (UDA) has shown remarkable results in bearing
fault 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, which
ignores the relation between subdomains. This paper addresses mentioned
challenges by presenting the novel deep subdomain adaptation graph convolution
neural network (DSAGCN), which has two key characteristics: First, graph
convolution 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 and
reduce structure discrepancy between relevant subdomains and global domains.
CWRU and Paderborn bearing datasets are used to validate the DSAGCN method's
efficiency and superiority between comparison models. The experimental results
demonstrate the significance of aligning structured subdomains along with
domain adaptation methods to obtain an accurate data-driven model in
unsupervised fault diagnosis.
