Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

Manjeevan Seera, Chee Peng Lim and Dahaman Ishak
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Chee Peng Lim: School of Computer Sciences, Universiti Sains Malaysia, Malaysia
Dahaman Ishak: School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Malaysia

International Journal of Natural Computing Research (IJNCR), 2012, vol. 3, issue 1, 44-55

Abstract: In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

Date: 2012
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