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A New MLP Approach for the Detection of the Incipient Bearing DamageSENGULER, T.   , KARATOPRAK, E. , SEKER, S. |
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Author keywords
bearing damage, vibration analysis, MLP neural networks, feature extraction, condition monitoring
References keywords
neural(16), networks(10), bearing(8), applications(7), signal(6), processing(6), artificial(6), monitoring(5), electric(5), condition(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2010-08-31
Volume 10, Issue 3, Year 2010, On page(s): 34 - 39
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.03006
Web of Science Accession Number: 000281805600006
SCOPUS ID: 77956621055
Abstract
In this study, it is aimed to track the aging trend of the incipient bearing damage in an induction motor which is subjected to an accelerated aging process. For this purpose, a new Multilayer perceptron (MLP) neural network approach is introduced. The input signals are extracted from power spectral densities (PSD) of the vibration signals taken from a 5-HP induction motor. Principal component analysis (PCA) has been applied to select the best possible feature vectors as a dimensionality reduction purpose. Variance and entropy values are used as the targets of the MLP network. The healthy motor condition was modelled by the MLP network considering all load conditions. The results showed that the incipient bearing damage was clearly extracted by the oscillations of the MLP output error. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
