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Doctoral Thesis
DOI
10.11606/T.18.2013.tde-29042013-114436
Document
Author
Full name
Fernanda Maria da Cunha Santos
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2013
Supervisor
Committee
Silva, Ivan Nunes da (President)
Bim, Edson
Creppe, Renato Crivellari
Leite, Luciana Cambraia
Suetake, Marcelo
Title in Portuguese
Identificação de falhas em motores de indução trifásicos usando sistemas inteligentes
Keywords in Portuguese
Identificação e diagnóstico de falhas
Motor de indução trifásico
Redes neurais artificiais
Sistemas inteligentes
Abstract in Portuguese
Esta tese consiste em desenvolver um sistema de identificação e classificação de falhas em motores de indução trifásico. As falhas analisadas foram simuladas em laboratório e envolvem problemas elétricos, como curto-circuito no estator, e problemas mecânicos, como barras quebradas no rotor. O sistema computacional proposto é formado pela transformada discreta wavelet, pelo cálculo de variáveis estatísticas e por redes neurais artificiais. A partir dos sinais elétricos da corrente do estator, a transformada wavelet produz os coeficientes característicos das falhas, os quais são usados no cálculo das variáveis estatísticas, como a média, root mean square, skewness e kurtosis. Estes valores são transmitidos como dados de entrada para as redes neurais que identificam as falhas e classificam a natureza das mesmas. Por fim, resultados obtidos visam validar a metodologia sugerida, que buscou nos sistemas inteligentes soluções eficazes para diagnosticar falhas em máquinas elétricas.
Title in English
Identification of faults in three-phase induction motors using intelligent systems
Keywords in English
Artificial neural networks
Faults diagnosis and identification
Intelligent system
Three-phase induction motor
Abstract in English
This thesis consists in developing a system for the identification and classification of faults in three-phase electric motors. The faults were analyzed and simulated in the laboratory and involve electrical problems, such as short circuit in the stator, and mechanical problems, such as broken rotor bars. The proposed computer system is formed by discrete wavelet transform, by calculation of statistical variables and for artificial neural networks. From the electrical signals of the stator current, the wavelet transform produces characteristic coefficients of faults, which are extracted by calculating of statistics variables, such as mean, root mean square, skewness and kurtosis. These values are passed as input to the neural networks that identify faults and the severity of it. Finally, results aimed at validating the methodology suggested that sought effective solutions in intelligent systems to diagnose faults in electrical machines.
 
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Fernanda.pdf (9.86 Mbytes)
Publishing Date
2013-05-08
 
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