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Multi-source domain adaptive network based on local kernelized higher-order moment matching for rotating machinery fault diagnosis.
- Source :
- ISA Transactions; Jul2024, Vol. 150, p311-321, 11p
- Publication Year :
- 2024
-
Abstract
- 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. • A multi-source domain adaptive network based on the adversarial theory is built. • A subdomain alignment method based on local kernelized higher-order moment matching. • A feature fusion strategy based on multi-branch local domain deviation metric. [ABSTRACT FROM AUTHOR]
- Subjects :
- FAULT diagnosis
ROTATING machinery
MAP design
Subjects
Details
- Language :
- English
- ISSN :
- 00190578
- Volume :
- 150
- Database :
- Supplemental Index
- Journal :
- ISA Transactions
- Publication Type :
- Academic Journal
- Accession number :
- 177873283
- Full Text :
- https://doi.org/10.1016/j.isatra.2024.04.031