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Doctoral Thesis
DOI
10.11606/T.18.2012.tde-26062012-164520
Document
Author
Full name
Marcelo Suetake
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2012
Supervisor
Committee
Silva, Ivan Nunes da (President)
Aguiar, Manoel Luís de
Caminhas, Walmir Matos
Creppe, Renato Crivellari
Gonzaga, Diógenes Pereira
Title in Portuguese
Sistemas inteligentes para monitoramento e diagnósticos de falhas em motores de indução trifásicos
Keywords in Portuguese
Identificação e diagnóstico de falhas
Motor de indução trifásico
Redes neurais artificiais
Sistemas de monitoramento
Sistemas inteligentes
Abstract in Portuguese
O objetivo desta tese consiste na implementação de sistemas inteligentes para monitoramento e diagnósticos de falhas ocorrentes em motores de indução trifásicos. Para tanto, desenvolveu-se uma bancada de experimentos que visa ensaios de falhas relacionados a curto-circuito entre as bobinas do enrolamento de estator, quebras nas barras da gaiola de esquilo do rotor e, finalmente, rolamentos defeituosos. Mais especificamente, o enfoque principal consiste na proposição de uma abordagem neural de detecção de quebras nas barras de rotores de motores de indução trifásicos mediante a análise do espectro de frequência e aplicação de técnicas de análise das componentes principais. Considerou-se o acionamento do motor de indução tanto pela tensão de alimentação da rede quanto por inversor trifásico em diferentes frequências, operando sob diversas condições de torque de carga para a avaliação da metodologia.
Title in English
Intelligent systems for faults monitoring and diagnosis in three-phase induction motors
Keywords in English
Artificial neural networks
Faults diagnosis and identification
Intelligent system
Monitoring system
Three-phase induction motor
Abstract in English
The objective of this thesis consists of the implementation of intelligent systems for three-phase induction motors fault diagnosis and condition monitoring. Therefore, an experimental test stand for stator winding inter-turn short circuit faults, broken rotor bar in squirrel cage and, finally, defective wheel bearing has been designed. The main focus is to propose a neural network approach, which uses spectral frequency analysis and principal component analysis techniques to detect broken rotor bar in squirrel cage induction motor. Induction motor operating at different load torque conditions and supplied with sinusoidal voltage supply and three-phase inverter at different frequency was considered in the experiment for methodology evaluation.
 
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Suetake.pdf (17.42 Mbytes)
Publishing Date
2012-07-03
 
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