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    Abstract
2012 (Vol. 4, Issue: 18)
Article Information:

Determination of Number of Broken Rotor Bars in Squirrel-Cage Induction Motors Using Adaptive Neuro-Fuzzy Interface System

Mehran Amani Juneghani, Babak Keyvani Boroujeni and Mostafa Abdollahi
Corresponding Author:  Mehran Amani Juneghani 

Key words:  Adaptive Neuro-Fuzzy Interface System (ANFIS), broken rotor bars, fault detection, Principal Component Analysis (PCA), wavelet, , ,
Vol. 4 , (18): 3399-3405
Submitted Accepted Published
April 04, 2012 April 25, 2012 September 15, 2012
Abstract:

For determination the number of broken rotor bars in squirrel-cage induction motors when these motors are working, this study presents a new method based on an intelligent processing of the stator transient starting current. In light load condition, distinguishing between safe and faulty rotors is difficult, because the characteristic frequencies of rotor with broken bars are very close to the fundamental component and their amplitudes are small in comparison. In this study, an advanced technique based on the Wavelet Adaptive Neuro-Fuzzy Interface System is suggested for processing the starting current of induction motors. In order to increase the efficiency of the proposed method, the results of the wavelet analysis, before applying to the Adaptive Neuro-Fuzzy Interface System, are processed by Principal Component Analysis (PCA). Then the outcome results are supposed as Adaptive Neuro-Fuzzy Interface System's training and testing data set. The trained Adaptive Neuro-Fuzzy Interface Systems undertake of determining the number of broken rotor bars. The given statistical results, announce the proposed methodís high ability to determine the number of broken rotor bars. The proposed method is independent from loading conditions of machine and it is useable even when the motor is unloaded.
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  Cite this Reference:
Mehran Amani Juneghani, Babak Keyvani Boroujeni and Mostafa Abdollahi, 2012. Determination of Number of Broken Rotor Bars in Squirrel-Cage Induction Motors Using Adaptive Neuro-Fuzzy Interface System.  Research Journal of Applied Sciences, Engineering and Technology, 4(18): 3399-3405.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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