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Deep learning neural networks for rolling bearing fault dia..:

, In: CSAA/IET International Conference on Aircraft Utility Systems (AUS 2022),

Shouhu, J. ; Yongwei, A. ; Guanwei, J.... - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.1749

RT 
T1 <cTitle>CSAA/IET International Conference on Aircraft Utility Systems (AUS 2022)</cTitle>: T1 <Title>Deep learning neural networks for rolling bearing fault diagnostic classification</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-9946219&Exemplar=1&LAN=DE
				
A1 Shouhu, J.
A1 Yongwei, A.
A1 Guanwei, J.
A1 Weiqing, X.
A1 Jia, W.
A1 Maolin, C.
YR 2022
K1 fault diagnosis
K1 recurrent neural nets
K1 rolling bearings
K1 safety
K1 vibrational signal processing
K1 convolutional neural nets
K1 deep learning (artificial intelligence)
K1 CWRU FE bearing dataset
K1 long short-time memory network
K1 cyclic convolutional neural network
K1 rotating machines
K1 economic benefits
K1 safe operations
K1 rolling bearing fault diagnostic classification
K1 LSTM
K1 deep learning neural networks
K1 CCNN
K1 WDCNN
K1 vibrational components
K1 first-layer large convolutional kernel convolutional neural network
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.1749
DO https://doi.org/10.1049/icp.2022.1749
SF ELIB - SuUB Bremen

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