Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information

Aobo Yu, Bolin Cai, Qiujie Wu, García, Miguel Martínez, Jing Li and Xiangcheng Chen

Reliability Engineering and System Safety, 2024, vol. 248, issue C

Abstract: Fault diagnosis is crucial for reliability assessment of rotation machinery. Due to issues such as data privacy, it is impossible to get complete information for fault diagnosis in practical and challenging scenario. To solve aforementioned problem, fault diagnosis under partial information is studied. A source-free domain adaptation method for fault diagnosis, enabling cross-domain fault diagnosis without accessing the source data, is proposed. Firstly, multireceptive field graph convolutional(MRF-GCN) was used to aggregate different numbers of node information from different receptive fields for extracting more representative features. Secondly during the training process on the target domain, positive and negative pairs are constructed based on the samples’ neighbors and extended neighbors. Clustering and domain adaptation are then performed using a contrastive loss. Finally, information maximization loss is employed to improve the diagnostic accuracy. Experimental results demonstrate that, the proposed approach achieves favorable diagnostic performance under partial information, even without access to source domain data.

Keywords: Partial information; Cross-domain fault diagnosis; Deep transfer learning; Source-free domain adaptation; Multireceptive field graph convolutional (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832024002552
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:248:y:2024:i:c:s0951832024002552

DOI: 10.1016/j.ress.2024.110181

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().