| Texto completo | |
| Autor(es): |
Godoy, Wagner Fontes
[1, 2]
;
da Silva, Ivan Nunes
[1]
;
Goedtel, Alessandro
[2]
;
Cunha Palacios, Rodrigo Henrique
[1, 2]
Número total de Autores: 4
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Sao Carlos Sch Engn, Dept Elect Engn, BR-13566590 Sao Carlos, SP - Brazil
[2] Fed Technol Univ Parana UTFPR, Dept Elect Engn, BR-86300000 Cornelio Procopio, PR - Brazil
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | APPLIED SOFT COMPUTING; v. 32, p. 420-431, JUL 2015. |
| Citações Web of Science: | 16 |
| Resumo | |
Three-phase induction motor are one of the most important elements of electromechanical energy conversion in the production process. However, they are subject to inherent faults or failures under operating conditions. The purpose of this paper is to present a comparative study among intelligent tools to classify short-circuit faults in stator windings of induction motors operating with three different models of frequency inverters. This is performed by analyzing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate according to machine frequency supply. To assess the classification accuracy across the various levels of faults severity, the performance of three different learning machine techniques were compared: (i) fuzzy ARTMAP network; (ii) multilayer perceptron network; and (iii) support vector machine. Results obtained from 2.268 experimental tests are presented to validate the study, which considered a wide range of operating frequencies and load conditions. (C) 2015 Elsevier B.V. All rights reserved. (AU) | |
| Processo FAPESP: | 11/17610-0 - Monitoramento e controle de sistemas dinâmicos sujeitos a falhas |
| Beneficiário: | Roberto Kawakami Harrop Galvão |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |