A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Autor(es)

Cunha Palacios, Rodrigo H. ^{[1, 2]} ; da Silva, Ivan Nunes ^[1] ; Goedtel, Alessandro ^[2] ; Godoy, Wagner F. ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Sch Engn, Dept Elect Engn, BR-13 56659 Sao Carlos, SP - Brazil

^[2] Fed Technol Univ Parana UTFPR, Dept Elect Engn, BR-86300000 Cornelli Procopio, PR - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Electric Power Systems Research; v. 127, p. 249-258, OCT 2015.
• Citações Web of Science: 43

Resumo

Three-phase induction motors are the key elements of electromechanical energy conversion for a variety of industrial sectors. The ability to identify motor faults before they occur can reduce the risks in decisions regarding machine maintenance, lower costs, and increase process availability. This article proposes a comprehensive evaluation of pattern classification methods for fault identification in induction motors. The methods discussed in this work are: Naive Bayes, k-Nearest Neighbor, Support Vector Machine (Sequential Minimal Optimization), Artificial Neural Network (Multilayer Perceptron), Repeated Incremental Pruning to Produce Error Reduction, and C4.5 Decision Tree. By analyzing the amplitudes of current signals in the time domain, experimental results with bearing, stator, and rotor faults are tested using different pattern classification methods under varied power supply and mechanical loading 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