Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information

Fault diagnosis is crucial for reliability assessment of rotation machinery. Due to issues such as data privacy, it is impossible to get complete information for fault diagnosis in practical and challenging scenario. To solve aforementioned problem, fault diagnosis under partial information is studied. A source-free domain adaptation method for fault diagnosis, enabling cross-domain fault diagnosis without accessing the source data, is proposed. Firstly, multireceptive field graph convolutional(MRF-GCN) was used to aggregate different numbers of node information from different receptive fields for extracting more representative features. Secondly during the training process on the target domain, positive and negative pairs are constructed based on the samples' neighbors and extended neighbors. Clustering and domain adaptation are then performed using a contrastive loss. Finally, information maximization loss is employed to improve the diagnostic accuracy. Experimental results demonstrate that, the proposed approach achieves favorable diagnostic performance under partial information, even without access to source domain data.