Neural approach for bearing fault detection in three phase induction motors

Author

Gongora, W.S. ; Silva, H.V.D. ; Goedtel, A. ; Godoy, W.F. ; da Silva, S.A.O.

Author_Institution

Dept. of Electr. Technician, IFPR Inst., Assis Chateaubriand, Brazil

fYear

2013

fDate

27-30 Aug. 2013

Firstpage

566

Lastpage

572

Abstract

The induction motor has been widely used in various industrial applications. Thus, several studies have presented strategies for the diagnosis and prediction of failures in these motor. One strategy used recently is based on intelligent systems, in particular, artificial neural networks. The purpose of this paper is to present an alternative tool to traditional methods for detection of bearing failures using on a perceptron network with signal analysis in time domain. Experimental results are presented to validate the proposal.

Keywords

electrical engineering computing; fault diagnosis; induction motors; perceptrons; artificial neural networks; bearing failures; bearing fault detection; failure diagnosis; failure prediction; industrial applications; intelligent systems; neural approach; perceptron network; signal analysis; three phase induction motors; time domain; Artificial neural networks; Induction motors; Maintenance engineering; Neurons; Noise; Training; Vibrations; Artificial Neural Networks; Failure prediction; Three phase induction motors;

fLanguage

English

Publisher

ieee

Conference_Titel

Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on

Conference_Location

Valencia

Type

conf

DOI

10.1109/DEMPED.2013.6645771

Filename

6645771