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A novel generative adversarial network based early fault di..:

, In: 3rd International Symposium on Electrical, Electronics and Information Engineering (ISEEIE 2023),

Luo, P. ; Yin, Z. ; Yuan, D.. - p. None , 2023

Link: https://doi.org/10.1049/icp.2023.1871

RT 
T1 <cTitle>3rd International Symposium on Electrical, Electronics and Information Engineering (ISEEIE 2023)</cTitle>: T1 <Title>A novel generative adversarial network based early fault diagnosis method for permanent magnet synchronous motor bearings</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10324550&Exemplar=1&LAN=DE
				
A1 Luo, P.
A1 Yin, Z.
A1 Yuan, D.
A1 Zhang, Y.
YR 2023
K1 convolutional neural nets
K1 fault diagnosis
K1 machine bearings
K1 mechanical engineering computing
K1 permanent magnet motors
K1 synchronous motors
K1 convolutional path unit
K1 CP-unit
K1 early fault characteristics
K1 early fault diagnosis method
K1 existing advanced methods
K1 fault samples
K1 network training requirements
K1 novel generative adversarial network
K1 permanent magnet synchronous motor bearing diagnosis method
K1 permanent magnet synchronous motor bearings
K1 PMSM bearing diagnosis method
K1 traditional fault diagnosis methods
K1 weak bearing feature information
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2023.1871
DO https://doi.org/10.1049/icp.2023.1871
SF ELIB - SuUB Bremen

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