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Mechanical fault diagnosis by using dynamic transfer adversarial learning

Authors :
Shaohui Zhang
Dejan Gjorgjevikj
Yadong Wei
Xiaoman Cai
Tuzhi Long
Chuan Li
Source :
Measurement Science and Technology. 32:104005
Publication Year :
2021
Publisher :
IOP Publishing, 2021.

Abstract

This research is partially supported by the National Natural Science Foundation of China (51975121, 51775112), the Guangdong Basic and Applied Basic Research Foundation (2019B1515120095), the MoST International Cooperation Program (6-14) and the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje.

Subjects

Subjects :
Engineering
business.industry
Applied Mathematics
Deep learning
Foundation (engineering)
Fault (power engineering)
Engineering management
Adversarial system
Computer Science and Engineering
Basic research
Natural science
Artificial intelligence
Transfer of learning
business
Instrumentation
Engineering (miscellaneous)

Details

ISSN :
13616501 and 09570233
Volume :
32
Database :
OpenAIRE
Journal :
Measurement Science and Technology
Accession number :
edsair.doi...........c5b02eae5bd679d9b10cbb1fa82793e6

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