Condition Monitoring of Broken Rotor Bars Using a Hybrid FMM-GA Model

被引：0

作者：

Seera, Manjeevan ^{[1
]}

Lim, Chee Peng ^{[2
]}

Loo, Chu Kiong ^{[1
]}

机构：

[1] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia

[2] Deakin Univ, Ctr Intelligent Syst Res, Geelong, Vic, Australia

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III | 2014年 / 8836卷

关键词：

Condition monitoring; fault diagnosis; fuzzy min-max neural network; genetic algorithms; induction motor; INDUCTION-MOTORS; NEURAL-NETWORKS; CLASSIFICATION; MAINTENANCE; FAULTS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don't care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

引用

页码：381 / 389

页数：9

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