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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

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  3/2010 - 6

 HIGH-IMPACT PAPER 

A New MLP Approach for the Detection of the Incipient Bearing Damage

SENGULER, T. See more information about SENGULER, T. on SCOPUS See more information about SENGULER, T. on IEEExplore See more information about SENGULER, T. on Web of Science, KARATOPRAK, E. See more information about  KARATOPRAK, E. on SCOPUS See more information about  KARATOPRAK, E. on SCOPUS See more information about KARATOPRAK, E. on Web of Science, SEKER, S. See more information about SEKER, S. on SCOPUS See more information about SEKER, S. on SCOPUS See more information about SEKER, S. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (818 KB) | Citation | Downloads: 1,045 | Views: 4,420

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
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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.


References | Cited By

Cited-By ISI Web of Science

Web of Science® Times Cited: 9 [View]
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Cited-By CrossRef

SCOPUS® Times Cited: 11
View record in SCOPUS®
[Free preview]

Updated today

Cited-By CrossRef

[1] Continuous wavelet transform for ferroresonance detection in power systems, Şengüler, Tayfun, Şeker, Serhat, Electrical Engineering, ISSN 0948-7921, Issue 2, Volume 99, 2017.
Digital Object Identifier: 10.1007/s00202-016-0387-0
[CrossRef]

[2] Acoustic Emission-Based Prognostics of Slow Rotating Bearing Using Bayesian Techniques Under Dependent and Independent Samples, Aye, S. A., Heyns, P. S., Applied Artificial Intelligence, ISSN 0883-9514, Issue 6, Volume 29, 2015.
Digital Object Identifier: 10.1080/08839514.2015.1038432
[CrossRef]

[3] Support vector machine classifier for diagnosis in electrical machines: Application to broken bar, Matić, Dragan, Kulić, Filip, Pineda-Sánchez, Manuel, Kamenko, Ilija, Expert Systems with Applications, ISSN 0957-4174, Issue 10, Volume 39, 2012.
Digital Object Identifier: 10.1016/j.eswa.2012.01.214
[CrossRef]

[4] State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis, MRUGALSKI, M., WITCZAK, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 12, 2012.
Digital Object Identifier: 10.4316/aece.2012.03010
[CrossRef] [Full text]

[5] Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network, Ben Ali, Jaouher, Chebel-Morello, Brigitte, Saidi, Lotfi, Malinowski, Simon, Fnaiech, Farhat, Mechanical Systems and Signal Processing, ISSN 0888-3270, Issue , 2015.
Digital Object Identifier: 10.1016/j.ymssp.2014.10.014
[CrossRef]

[6] Analysis of statistical features based on start-up current envelope for broken rotor bar fault detection in line start permanent magnet synchronous motor, Mehrjou, Mohammad Rezazadeh, Mariun, Norman, Misron, Norhisam, Radzi, Mohd Amran Mohd, Electrical Engineering, ISSN 0948-7921, Issue 1, Volume 99, 2017.
Digital Object Identifier: 10.1007/s00202-016-0404-3
[CrossRef]

[7] Vibration Based Broken Bar Detection in Induction Machine for Low Load Conditions, MATIC, D., KANOVIC, Z., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.01007
[CrossRef] [Full text]

[8] Radial basis function neural network based comparison of dimensionality reduction techniques for effective bearing diagnostics, GS, Vijay, Pai, Srinivasa P, Sriram, NS, Rao, Raj BKN, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, ISSN 1350-6501, Issue 6, Volume 227, 2013.
Digital Object Identifier: 10.1177/1350650112464927
[CrossRef]

[9] Broken rotor bar fault detection in line start permanent magnet synchronous motor using transient current signal, Izadi, Mahdi, Mariun, Norman, Mehrjou, Mohammad Rezazadeh, Abidin Ab Kadir, Mohd Zainal, Misron, Norhisam, Radzi, Mohd Amran Mohd, 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), ISBN 978-1-5090-4186-2, 2016.
Digital Object Identifier: 10.1109/I2CACIS.2016.7885288
[CrossRef]

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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.

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