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Active fault detection using time and frequency diagnostic ..:

, In: 11th International Conference on Power Electronics, Machines and Drives (PEMD 2022),

El-Dalahmeh, M. ; Al-Greer, M. ; Demirel, A.. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.1084

RT 
T1 <cTitle>11th International Conference on Power Electronics, Machines and Drives (PEMD 2022)</cTitle>: T1 <Title>Active fault detection using time and frequency diagnostic features for electrical machine</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-9868688&Exemplar=1&LAN=DE
				
A1 El-Dalahmeh, M.
A1 Al-Greer, M.
A1 Demirel, A.
A1 Keysan, O.
YR 2022
K1 electric machine analysis computing
K1 fault diagnosis
K1 feature extraction
K1 learning (artificial intelligence)
K1 permanent magnet motors
K1 stators
K1 synchronous motors
K1 fault conditions
K1 highly accurate prediction results
K1 different motor states
K1 interturn short-circuit fault states
K1 active fault detection
K1 frequency diagnostic features
K1 electrical machine
K1 accurate fault detection
K1 electrical motors
K1 system reliability
K1 effective diagnosis method
K1 permanent magnet synchronous motors
K1 wide speed
K1 load range
K1 fault-detection method
K1 stator currents
K1 frequency domains
K1 machine-learning algorithm
K1 PMSM
K1 time diagnostic features
K1 extracted features
K1 short-circuit fault states
K1 subspace discriminant tree
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.1084
DO https://doi.org/10.1049/icp.2022.1084
SF ELIB - SuUB Bremen

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