Multi-source domain adaptive network based on local kernelized higher-order moment matching for rotating machinery fault diagnosis.

作者：Ying Zhang, Jingjing Fan, Zong Meng, Jimeng Li, Wei Cao, Huihui He, Zhaohui Zhang, Fengjie Fan · 发表于：ISA transactions · 年份：2024 · DOI：10.1016/j.isatra.2024.04.031 · 被引用次数：21 · 研究领域：Medicine

Unsupervised domain adaptation has been extensively researched in rotating-machinery cross-domain fault diagnosis. A multi-source domain adaptive network based on local kernelized higher-order moment matching is constructed in this research for rotating-machinery fault diagnosis. Firstly, a multi-branch network is designed to map each source-target pair to a domain-specific shared space and to extract domain-invariant features using domain adversarial thought. Then, a local kernelized higher-order moment matching algorithm is proposed to perform fine-grained matching in shared category subspace. Finally, a feature fusion strategy based on the local domain distribution deviation is applied to synthesize the output features of multiple classifiers to obtain diagnostic results. The experimental validation of two-branch and three-branch networks on two public datasets is carried out and average diagnostic accuracies both exceed 99%. The results demonstrate the effectiveness and superiority of the approach.