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

Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

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

4 Author(s)
Masrur, M.A. ; US Army RDECOM-TARDEC, Warren, MI ; Zhihang Chen ; Zhang, B. ; Murphey, Y.L.

This paper presents research in model based fault diagnostics for the power electronics inverter based induction motor drives. A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis. Instead of simple open-loop circuits, our research focuses on closed loop circuits. Our simulation experiments show that this model-based fault diagnostic approach is effective in detecting single switch open-circuit faults as well as post-short-circuit conditions occurring in power electronics inverter based electrical drives.

Published in:

Power Engineering Society General Meeting, 2007. IEEE

Date of Conference:

24-28 June 2007

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.