Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics Tarek AROUI, Yassine KOUBAA, Ahmed TOUMI Leonardo J Sci 2009; 8(15):1-14 ICID: 900997
Article type: Original article
IC™ Value: 4.48
images/publishers/1997.gif
Abstract provided by Publisher
Self-Organizing Maps (SOM) is an excellent method of analyzing multidimensional data. The SOM based classification is attractive, due to its unsupervised learning and topology preserving properties. In this paper, the performance of the self-organizing methods is investigated in induction motor rotor fault detection and severity evaluation. The SOM is based on motor current signature analysis (MCSA). The agglomerative hierarchical algorithms using the Ward’s method is applied to automatically dividing the map into interesting interpretable groups of map units that correspond to clusters in the input data. The results obtained with this approach make it possible to detect a rotor bar fault just directly from the visualization results. The system is also able to estimate the extent of rotor faults.
Index Copernicus 2010. All rights reserved. Design by ww design
IndexCopernicus is property of Index Copernicus International S.A.. Copying information, in part or in whole by any means is prohibited without a written permission from the owner. The way of linking the service has to be agreed with the IT Department of Index Copernicus International S.A. (webmaster@indexcopernicus.com).
