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Fault diagnosis of rolling bearing under multiple working c..:

, In: 12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022),

Li, X.-L. ; Jiang, G.-J. ; Wang, Z. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.3082

RT 
T1 <cTitle>12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)</cTitle>: T1 <Title>Fault diagnosis of rolling bearing under multiple working conditions based on multi-scale kernel convolution neural network</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10110689&Exemplar=1&LAN=DE
				
A1 Li, X.-L.
A1 Jiang, G.-J.
A1 Wang, Z.
YR 2022
K1 convolutional neural nets
K1 deep learning (artificial intelligence)
K1 fault diagnosis
K1 feature extraction
K1 learning (artificial intelligence)
K1 mechanical engineering computing
K1 probability
K1 rolling bearings
K1 vibrational signal processing
K1 vibrations
K1 wind turbines
K1 angular domain re-sampling intelligent fault diagnosis model
K1 classification performance
K1 combination model
K1 deep convolutional neural network
K1 excellent feature learning ability
K1 failure mode
K1 fault classification
K1 gearbox rolling bearings
K1 high failure incidence probability
K1 inception model
K1 information fusion
K1 mechanical vibration signals
K1 multiple branches
K1 multiple working conditions
K1 multiscale kernel convolution neural network
K1 multiscale kernel depth
K1 nonlinear
K1 raw signal signs
K1 rotational speed changes
K1 running process
K1 uniform speed states
K1 variable rotational speed states
K1 wind turbine
K1 working environment
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.3082
DO https://doi.org/10.1049/icp.2022.3082
SF ELIB - SuUB Bremen

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