Improving elman network using genetic algorithm for bearing failure diagnosis of induction motor

2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2009

Volumn , Issue , 2009, Pages

  Mahamad, Abd Kadir ^a   Hiyama, Takashi ^a  

^a KUMAMOTO UNIVERSITY   (Japan)

Author keywords

1MB; ANN; Distance evaluation technique; EN; Fault diagnosis; GA

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BEARING FAILURES; CASE WESTERN RESERVE UNIVERSITY; DETECTION AND DIAGNOSIS; ELMAN NETWORK; FAULT DIAGNOSIS; FEATURES SELECTION; FREQUENCY DOMAINS; HIGH FREQUENCY COMPONENTS; TIME DOMAIN; VIBRATION SIGNAL;

BEARINGS (STRUCTURAL); DAMAGE DETECTION; ELECTRIC DRIVES; ELECTRIC MACHINERY; FAILURE ANALYSIS; FAST FOURIER TRANSFORMS; GENETIC ALGORITHMS; MOTORS; NEURAL NETWORKS; POWER ELECTRONICS;

INDUCTION MOTORS;

EID: 74349085061     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DEMPED.2009.5292794     Document Type: Conference Paper

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