A Neuro-Fuzzy Approach for Locating Broken Rotor Bars in Induction Motors at Very Low Slip

A Neuro-Fuzzy Approach for Locating Broken Rotor Bars in Induction Motors at Very Low Slip Squirrel-cage induction motors are widely used in a number of applications throughout the world. This paper proposes a neuro-fuzzy approach to identify and to classify a typical fault related to the induction motor damage, such as broken rotor bars. Two fuzzy classifiers are obtained by an adaptive-network-based fuzzy inference system model whose parameters can be identified by using the hybrid learning algorithm. A Hall effect sensor was installed between two stator slots of the induction machine, and a magnetic flux density variation is measured according to the failure. The data from the Hall sensor were used to extract some harmonic components by applying fast Fourier transform. Thus, some frequencies and their amplitudes were considered as inputs for the proposed fuzzy model to detect not only adjacent broken bars, but also noncontiguous faulted scenarios. In the present work it is not necessary to estimate the rotor slip, as required by the traditional condition monitoring technique, known as motor current signature analysis. This method was able to detect broken bars for induction motor running at low-load or no-load condition. The intelligent approach was validated using some experimental data from a 7.5-kW squirrel-cage induction machine. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Control, Automation and Electrical Systems Springer Journals

A Neuro-Fuzzy Approach for Locating Broken Rotor Bars in Induction Motors at Very Low Slip

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Brazilian Society for Automatics--SBA
Subject
Engineering; Electrical Engineering; Control, Robotics, Mechatronics; Control; Robotics and Automation
ISSN
2195-3880
eISSN
2195-3899
D.O.I.
10.1007/s40313-018-0388-5
Publisher site
See Article on Publisher Site

Abstract

Squirrel-cage induction motors are widely used in a number of applications throughout the world. This paper proposes a neuro-fuzzy approach to identify and to classify a typical fault related to the induction motor damage, such as broken rotor bars. Two fuzzy classifiers are obtained by an adaptive-network-based fuzzy inference system model whose parameters can be identified by using the hybrid learning algorithm. A Hall effect sensor was installed between two stator slots of the induction machine, and a magnetic flux density variation is measured according to the failure. The data from the Hall sensor were used to extract some harmonic components by applying fast Fourier transform. Thus, some frequencies and their amplitudes were considered as inputs for the proposed fuzzy model to detect not only adjacent broken bars, but also noncontiguous faulted scenarios. In the present work it is not necessary to estimate the rotor slip, as required by the traditional condition monitoring technique, known as motor current signature analysis. This method was able to detect broken bars for induction motor running at low-load or no-load condition. The intelligent approach was validated using some experimental data from a 7.5-kW squirrel-cage induction machine.

Journal

Journal of Control, Automation and Electrical SystemsSpringer Journals

Published: May 30, 2018

References

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