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A two-stage feature selection for rolling bearing fault dia..:

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

Zhao, B. ; Dai, W. ; Chen, Y. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.3086

RT 
T1 <cTitle>12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)</cTitle>: T1 <Title>A two-stage feature selection for rolling bearing fault diagnosis using ReliefF and SVM-RFE</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10110464&Exemplar=1&LAN=DE
				
A1 Zhao, B.
A1 Dai, W.
A1 Chen, Y.
YR 2022
K1 condition monitoring
K1 fault diagnosis
K1 feature extraction
K1 feature selection
K1 mechanical engineering computing
K1 pattern classification
K1 rolling bearings
K1 support vector machines
K1 bearing fault diagnosis
K1 distinguishable features
K1 Euclidean distance
K1 fault data
K1 irrelevant features
K1 multiclass support vector machine
K1 multidomain feature set
K1 redundant features
K1 ReliefF algorithm
K1 rolling bearing public datasets
K1 sample data
K1 support vector machine recursive feature elimination
K1 SVM-RFE
K1 SVMRFE
K1 two-stage feature selection method
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.3086
DO https://doi.org/10.1049/icp.2022.3086
SF ELIB - SuUB Bremen

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