By Topic

Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach

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

2 Author(s)
Nejjari, H. ; Ecole Nat. Superieure des Arts et Metiers, Lille, France ; Benbouzid, M.E.H.

Various applications of artificial neural networks (ANNs) presented in the literature prove that such technique is well suited to cope with online fault diagnosis in induction motors. The aim of this paper is to present a methodology by which induction motor electrical faults can be diagnosed. The proposed methodology is based on the so-called Park's vector approach. In fact, stator current Park's vector patterns are first learned, using ANN's, and then used to discern between “healthy” and “faulty” induction motors. The diagnosis process was tested on both classical and decentralized approaches. The purpose of a decentralized architecture is to facilitate a satisfactory distributed implementation of new types of faults to the initial NN monitoring system. The generality of the proposed methodology has been experimentally tested on a 4 kW squirrel-cage induction motor. The obtained results provide a satisfactory level of accuracy, indicating a promising industrial application of the hybrid Park's vector-neural networks approach

Published in:

Industry Applications, IEEE Transactions on  (Volume:36 ,  Issue: 3 )