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Fault diagnosis of wind turbine bearing using a multi-scale..:
Xu, Z
;
Mei, X
;
Wang, X
...
https://researchonline.ljmu.ac.uk/id/eprint/20243/1/Revison_RE.pdf. , 2022
Link:
http://researchonline.ljmu.ac.uk/id/eprint/20243/
RT Journal T1 <Title>Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftliverpooljmu:oai:researchonline.ljmu.ac.uk:20243&Exemplar=1&LAN=DE A1 Xu, Z A1 Mei, X A1 Wang, X A1 Yue, M A1 Jin, J A1 Yang, Y A1 Li, C PB Elsevier YR 2022 K1 TA Engineering (General). Civil engineering (General) K1 TD Environmental technology. Sanitary engineering K1 TJ Mechanical engineering and machinery K1 TK Electrical engineering. Electronics. Nuclear engineering JF https://researchonline.ljmu.ac.uk/id/eprint/20243/1/Revison_RE.pdf LK http://researchonline.ljmu.ac.uk/id/eprint/20243/ DO http://researchonline.ljmu.ac.uk/id/eprint/20243/ SF ELIB - SuUB Bremen
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