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Abstract
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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
Submitted: April 04, 2012
Accepted: April 25, 2012
Published: September 15, 2012 |
Abstract:
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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 method’s 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.
Key words: Adaptive Neuro-Fuzzy Interface System (ANFIS), broken rotor bars, fault detection, Principal Component Analysis (PCA), wavelet, , ,
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Cite this Reference:
Mehran Amani Juneghani, Babak Keyvani Boroujeni and Mostafa Abdollahi, . 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, (18): 3399-3405.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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