Transfer Graph-Driven Rotating Machinery Diagnosis Considering Cross-Domain Relationship Construction

Transfer learning-based fault diagnosis methods borrow source-domain knowledge to achieve diagnosis task for the unlabeled target domain. However, existing research articles mainly lie in feature mapping and model transfer, ignoring the relationship between cross-domain samples. Once connections between cross-domain samples with the same label can be constructed, label propagation will be easier even if there is a cross-domain distribution discrepancy. In this article, a transfer graph-driven rotating machinery diagnosis considering cross-domain relationship construction is proposed. Specifically, signal spectrum is extracted by fast Fourier transform mapping raw signals to identical feature space. Transfer graphs are constructed by the Euclidean distance between nodes, where the relationships between the same domain samples, even cross-domain samples, are established. Then, the graph convolutional network (GCN), trained by source-domain samples and less target-domain samples, is utilized for cross-domain diagnosis tasks. The experimental results demonstrate the effectiveness of the proposed method. In addition, trained GCN enables diagnosing on newly constructed target-domain graphs. It shows the ability to continuously learn new transferable knowledge.