Statistical and neural-network approaches for the classification of induction machine faults using the ambiguity plane representation

IEEE Transactions on Industrial Electronics

Volumn 60, Issue 9, 2013, Pages 4034-4042

  Boukra, Tahar ^a,b   Lebaroud, Abdesselam ^a,b   Clerc, Guy ^c  

^a UNIVERSITY OF SKIKDA   (Algeria)

^b Laboratoire de Génie Electrique Constantine   (Algeria)

^c UNIVERSITÉ LYON 1   (France)

Author keywords

Ambiguity plane; artificial neural networks (ANNs); discrete wavelet transforms; distance measurement; error probability; fault diagnosis; frequency domain analysis; induction motors; time domain analysis; time frequency domain analysis

Indexed keywords

DISCRETE WAVELET TRANSFORMS; DISTANCE MEASUREMENT; FAILURE ANALYSIS; FAULT DETECTION; FREQUENCY DOMAIN ANALYSIS; INDUCTION MOTORS; TIME DOMAIN ANALYSIS;

AMBIGUITY PLANE; CLASSIFICATION ALGORITHM; CLASSIFICATION RATES; ERROR PROBABILITIES; FISHER'S DISCRIMINANT; INDUCTION MACHINES; INDUCTION MOTOR TESTS; TIME-FREQUENCY-DOMAIN ANALYSIS;

NEURAL NETWORKS;

EID: 84877731969     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2012.2216242     Document Type: Article

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