Neural Approach for Bearing Fault Detection in Three Phase Induction Motors

被引：0

作者：

Gongora, W. S. ^{[1
]}

Silva, H. V. D. ^{[2
]}

Goedtel, A. ^{[3
]}

Godoy, W. F. ^{[3
]}

da Silva, S. A. O. ^{[4
]}

机构：

[1] IFPR Inst, Dept Elect Tech, BR-85935000 Assis Chateaubriand, PR, Brazil

[2] UNOPAR Univ, Dept Elect Engn, BR-86041120 Londrina, PR, Brazil

[3] UTFPR Univ, Dept Elect Engn, BR-86300000 Cornelio Procopio, PR, Brazil

[4] UTFPR CP Univ, Dept Elect Engn, BR-86300000 Cornelio Procopio, PR, Brazil

来源：

2013 9TH IEEE INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRIC MACHINES, POWER ELECTRONICS AND DRIVES (SDEMPED) | 2013年

关键词：

Artificial Neural Networks; Failure prediction; Three phase induction motors; DIAGNOSIS; NETWORKS;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：566 / 572

页数：7

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