Cart (Loading....) | Create Account
Close category search window
 

FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors Through Information Entropy and Fuzzy Inference

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

8 Author(s)
Romero-Troncoso, R.J. ; HSPdigital-CA Telematica/Procesamiento Digital de Senales, Univ. de Guanajuato, Guanajuato, Mexico ; Saucedo-Gallaga, R. ; Cabal-Yepez, E. ; Garcia-Perez, A.
more authors

The development of monitoring systems for rotating machines is the ability to accurately detect different faults in an incipient state. The most popular rotating machine in industry is the squirrel-cage induction motor, and the failure on such motors may have severe consequences in costs, product quality, and safety. Most of the condition-monitoring techniques for induction motors focus on a single specific fault. The identification of two or more combined faults has been rarely considered, in spite of being a very usual situation in real rotary machines. On the other hand, information entropy is a signal processing technique that has recently proved its suitability for fault detection on induction motors, and fuzzy logic analysis has extensively been used in combination with several processing techniques in improving the diagnosis of a single isolated fault. The contribution of this paper is a novel methodology that is suitable for hardware implementation, which merges information entropy analysis with fuzzy logic inference to identify faults like bearing defects, unbalance, broken rotor bars, and combinations of faults by analyzing one phase of the induction motor steady-state current signal. The proposed methodology shows satisfactory results that prove its suitability for online detection of single and multiple combined faults in an automatic way through its hardware implementation in a field programmable gate array device.

Published in:

Industrial Electronics, IEEE Transactions on  (Volume:58 ,  Issue: 11 )

Date of Publication:

Nov. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.