Semisupervised Subdomain Adaptation Graph Convolutional Network for Fault Transfer Diagnosis of Rotating Machinery Under Time-Varying Speeds

Abstract

The deep learning-based fault diagnosis approaches have shown great advantages in ensuring rotating machinery (RM) work normally and safely. However, in real industrial applications, due to the influence of speed fluctuation, the differences in distribution of training samples and testing samples are inevitable, which will greatly affect the diagnosis result of the model. In the article, a novel end-to-end method for the fault transfer diagnosis of RM under time-varying speeds, named semisupervised subdomain adaptation graph convolutional network (SSAGCN) is proposed by integrating SSA and GCN. To begin with, a feature extractor based on GCN is designed to obtain the transferable information of the source domain (SD) and target domain (TD) data. In addition, the closely watched fault diagnosis (FD) approach based on global domain shift and unsupervised domain adaptation is improved by employing an adaptive layer based on SSA to reduce the distribution difference of the same fault type in SD and TD. The proposed SSAGCN approach takes full advantage of the powerful capability of GCN in capturing the relationship between signals, and the excellent performance of SSA in the use of unlabeled samples and idle labeled samples, thus overcoming the distribution differences caused by speed fluctuation. Two experimental cases are carried out to prove its effectiveness under time-varying speeds, and their results indicate our presented SSAGCN approach can realize more excellent performance on diagnosis accuracy and model complexity compared with existing methods.