A Multi-Source Domain Transfer Fault Diagnosis Method Based on Graph Embedding and Adaptive Classifier

To address the prevalent domain inadaptation issue in multi-source domain transfer fault diagnosis, this study proposes a novel model integrating graph embedding technology and adaptive classifiers. The framework combines multi-statistic multi-scale permutation entropy methods to simultaneously optimize projection and classifier performance, thereby enhancing generalization capabilities and diagnostic accuracy. During model construction, a weighted non-parametric maximum mean difference approach is employed to quantify and minimize differences in edge distributions and conditional distributions between source and target domains. Through similarity weight allocation, norm adjustment, and row sparsity learning strategies, the model effectively reduces interference from irrelevant samples. Additionally, by embedding category information and domain manifold structures, the model ensures classification validity of mapped features. Using labeled source domain data and unlabeled target domain data, the study develops a structure-aware adaptive classifier that narrows distribution differences while preserving manifold consistency, significantly improving diagnostic performance under variable operating conditions. Experimental results demonstrate that this multi-source domain transfer model achieves higher accuracy than single-domain approaches. Notably, in rotating machinery variable operating scenarios, the proposed method outperforms existing techniques in both precision and generalization capabilities.