| Full text | |
| Author(s): |
Godoy, Wagner Fontes
[1, 2]
;
da Silva, Ivan Nunes
[1]
;
Goedtel, Alessandro
[2]
;
Cunha Palacios, Rodrigo Henrique
[1, 2]
Total Authors: 4
|
| Affiliation: | [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
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | APPLIED SOFT COMPUTING; v. 32, p. 420-431, JUL 2015. |
| Web of Science Citations: | 16 |
| Abstract | |
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) | |
| FAPESP's process: | 11/17610-0 - Monitoring and control of dynamic systems subject to faults |
| Grantee: | Roberto Kawakami Harrop Galvão |
| Support Opportunities: | Research Projects - Thematic Grants |