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Graph data-driven methods have gradually attracted attention in transfer learning-based machine fault diagnosis. However, there are still some limitations. First, feature space deviation exists in the mapping of relationship information in the source and target domains during the graph construction, bringing negative transfer and limiting constructed graph quality. Second, interpretability of relationship information during graph construction for machine fault diagnosis is lacking. In this article, a dynamic graph-driven rotating machine fault diagnosis method via adaptively updating cross-domain relationship information is proposed. A dynamic transfer graph (DTG) construction framework is developed to keep the relationship information mapping in the cross-domain consistent. Meanwhile, an improved classification loss, which consists of multiscale cross-entropy loss and multiscale domain adaptation loss, is designed to construct high-quality DTG. In addition, the working mechanism of relationship information in DTG is revealed by exploring the changes of intraclass edges, interclass edges, and cross-domain edge connections in the graphs. Experimental results demonstrate its effectiveness.
Yang et al. (Fri,) studied this question.