Identificador de falhas para motor de indução trifásico baseado em support vector machine implementado em cloud.

Carlos Vinicius Alves CoimbraJacyeude De Morais Passos Araújo Segundo

Abstract

Ao passo que a Indústria 4.0 avança, conjuntos de ações de automação e controle vem sendo implementados. Dentro deste contexto o sensoriamento de motores de indução trifásicos vem se tornando remoto e conectado à internet. A manutenção preventiva pode então utilizar esse grande volume de dados para aumentar sua capacidade de detecção de falhas em relação aos métodos clássicos de classificação. Este trabalho propõe o desenvolvimento de um identificador de diferentes condições, entre normalidade, desbalanceamento no rotor, alimentação por duas fases e desníveis na base de um motor trifásico de indução W22 IR3, com base em dados de análises vibracionais e de correntes elétricas. Utilizando um sistema para aquisição de dados que consiste em um acelerômetro MEMS (Microelectromechanical Systems) e um transformador de corrente não invasivo SCT-013, controlados por um SoC (System on Chip). A análise dos dados foi realizada na IBM Cloud através de Watson Studio e SPSS Modeler para aplicação de um modelo estatístico Support Vectot Machine (SVM) que foi treinado e testado usando diferentes funções kernel. Observou-se que a oferta da escolha das funções kernel condicionam os dados a diferentes performances de processamento. A utilização do...Continue Reading

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