Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Feb 2019
Next issue: May 2019
Avg review time: 79 days


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


TRAFFIC STATS

2,185,742 unique visits
571,515 downloads
Since November 1, 2009



Robots online now
SemanticScholar


SJR SCImago RANK

SCImago Journal & Country Rank


SEARCH ENGINES

aece.ro - Google Pagerank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 19 (2019)
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
 Volume 15 (2015)
 
     »   Issue 4 / 2015
 
     »   Issue 3 / 2015
 
     »   Issue 2 / 2015
 
     »   Issue 1 / 2015
 
 
  View all issues  








LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  1/2018 - 12

Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis

PANIGRAHY, P. S. See more information about PANIGRAHY, P. S. on SCOPUS See more information about PANIGRAHY, P. S. on IEEExplore See more information about PANIGRAHY, P. S. on Web of Science, CHATTOPADHYAY, P. See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. 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 (8,079 KB) | Citation | Downloads: 216 | Views: 531

Author keywords
discrete wavelet transforms, fault diagnosis, feature extraction, induction motors, machine learning

References keywords
induction(17), fault(13), diagnosis(9), motors(8), garcia(8), detection(8), perez(7), motor(7), analysis(7), wavelet(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 95 - 104
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01012
Web of Science Accession Number: 000426449500012
SCOPUS ID: 85043281619

Abstract
Quick view
Full text preview
Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.


References | Cited By  «-- Click to see who has cited this paper

[1] S. Nandi, H. A. Toliyat, X. Li, "Condition monitoring and fault diagnosis of electrical motors-a review," IEEE trans. energy convers, vol. 20, pp. 719-729, 2005.
[CrossRef] [Web of Science Times Cited 908] [SCOPUS Times Cited 1207]


[2] P. Konar, P. Chattopadhyay, "Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)," Appl. Soft Computing, vol. 11, pp. 4203-4211, 2011.
[CrossRef] [Web of Science Times Cited 145] [SCOPUS Times Cited 180]


[3] S. Sridhar, K. Uma Rao, S. Jade, "Detection of broken rotor bar fault in induction motor at various load conditions using wavelet transforms," IEEE Int. Conf. Recent Developments in Control, Automation and Power Engineering, 2015, pp. 77–82.
[CrossRef] [SCOPUS Times Cited 3]


[4] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets," IEEE Trans. Power Electronics, vol. 29, pp. 936-945, 2014.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 115]


[5] P. A. Delgado-Arredondo, A. Garcia-Perez, D. Morinigo-Sotelo, "Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient," Shock and Vibration, vol. 2015, pp. 14, 2015.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 20]


[6] E. Cabal-Yepez, M. Valtierra-Rodriguez, R. J. Romero-Troncoso, A. Garcia-Perez, R.A. Osornio-Rios, H. Miranda-Vidales, R. Alvarez-Salas, "FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors," Mech. Sys. and Sig. Process, vol. 30, pp. 123-130, 2012.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 33]


[7] P. Konar, J. Sil, P. Chattopadhyay, "Knowledge extraction using data mining for multi-class fault diagnosis of induction motor," Neurocomputing, vol. 166, pp. 14-25, 2015.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]


[8] P. Konar, P. Chattopadhyay, "Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform," Appl. Soft Computing, vol. 30, pp. 341-352, 2015.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 31]


[9] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Vibration analysis based interturn fault diagnosis in induction machines," IEEE Trans. Ind. Inf, vol. 10, pp. 340-350, 2014.
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 67]


[10] R. J. Romero-Troncoso, R. Saucedo-Gallaga, E. Cabal-Yepez, A. Garcia-Perez, R. H. Osornio-Rios, R. Alvarez-Salas, H. Miranda-Vidales, N. Huber, "FPGA-based online detection of multiple combined faults in induction motors through information entropy and fuzzy inference," IEEE Trans. Ind. Elect, vol. 58, pp. 5263-5270, 2011.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 78]


[11] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Incipient interturn fault diagnosis in induction machines using an analytic wavelet-based optimized bayesian inference," IEEE trans. neural networks and learning sys, vol. 25, pp. 990-1001, 2014.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]


[12] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Single-turn fault detection in induction machine using complex-wavelet-based method," IEEE Trans. Ind. Appl, vol. 48, pp. 1846-1854, 2012.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 18]


[13] H. Garcia-Perez, R. d. J. Romero-Troncoso, E. Cabal-Yepez , R. A. Osornio-Rios, "The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors IEEE Trans. Ind. Electronics, vol. 58, pp. 2002-2010, 2011.
[CrossRef] [Web of Science Times Cited 111] [SCOPUS Times Cited 127]


[14] H. Ordaz-Moreno, R. d. J. Romero-Troncoso, J. A. Vite-Frias, J. R. Rivera-Gillen, A. Garcia-Perez, "Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation," IEEE Trans. Ind. Elec, vol. 55, pp. 2193-2202, 2008.
[CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 176]


[15] E. Cabal-Yepez, A. G. Garcia-Ramirez, R. J. Romero-Troncoso, A. Garcia-Perez, Roque A. Osornio-Rios, "Reconfigurable monitoring system for time-frequency analysis on industrial equipment through STFT and DWT," IEEE Trans. Ind. Inf, vol. 9, pp. 760-771, Jan. 2013.
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 71]


[16] H. Garcia-Perez, R. J. Romero-Troncoso, E. Cabal-Yepez, R. A. Osornio-Rios, J. d. J. Rangel-Magdaleno, H. Miranda, "Startup current analysis of incipient broken rotor bar in induction motors using high-resolution spectral analysis," IEEE Symp. Diagnostics for Electrical Machines, pp. 657-663, 2011.
[CrossRef] [SCOPUS Times Cited 31]


[17] H. M. Knight, S. P. Bertani, "Mechanical fault detection in a medium-sized induction motor using stator current monitoring," IEEE Trans. Energy Conv, vol. 20, pp. 753-760, 2005.
[CrossRef] [Web of Science Times Cited 83] [SCOPUS Times Cited 93]


[18] P. Zhang, Y. Du, T. G. Habetler, B. Lu, "A survey of condition monitoring and protection methods for medium-voltage induction motors," IEEE Trans. Ind. Appl, vol. 47, pp. 34-46, 2011.
[CrossRef] [Web of Science Times Cited 282] [SCOPUS Times Cited 334]


[19] P. S. Panigrahy, P. Konar, P. Chattopadhyay, "Application of data mining in fault diagnosis of induction motor," IEEE Int. Conf. Control, Measurement and Instrumentation, 2016, pp. 274-278.
[CrossRef] [SCOPUS Times Cited 5]


[20] P. Konar, P. S. Panigrahy, P. Chattopadhyay, "Tri-Axial Vibration Analysis Using Data Mining for Multi Class Fault Diagnosis in Induction Motor," Int. Conf. Mining Intelligence and Knowledge Exploration Springer International Publishing, 2015, pp. 553–562.
[CrossRef] [SCOPUS Times Cited 3]


[21] M. Kantardzic, Data mining: concepts, models, methods, and algorithms. Second Ed., John Wiley & Sons, 2011.
[CrossRef] [SCOPUS Times Cited 363]


[22] H. Jurek, Y. Bi, S. Wu, C. D. Nugent, "Clustering-based ensembles as an alternative to stacking," IEEE Trans. Knowledge and Data Eng, vol. 26, pp. 2120-2137, 2014.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


[23] J. Xia, L. Bombrun, T. Adali, Y. Berthoumieu, C. Germain, "Spectral–Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy," IEEE Trans. Geoscience and Remote Sensing, vol. 54, pp. 4971-4982, 2016.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 32]


[24] S. Dzeroski, B. Zenko. Is combining classifiers with stacking better than selecting the best one?. Machine learning, pp. 255-273, 2004.
[CrossRef] [Web of Science Times Cited 245] [SCOPUS Times Cited 336]


[25] S. Arlot, A. Celisse. A survey of cross-validation procedures for model selection. Statistics surveys, pp. 40-79, 2010.
[CrossRef] [SCOPUS Times Cited 1180]


[26] Z. Deng, F. L. Chung, S. Wang, "Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization," IEEE Trans. on Fuzzy Systems, vol. 18, pp. 726-744, 2010.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 34]


[27] A. Ambarwari, Y. Herdiyeni and T. Djatna, "Combination of Relief Feature Selection and Fuzzy K-Nearest Neighbor for Plant Species Identification," Int. Conf. on Advanced Computer Science and Information Systems, 2016, pp. 315–320.
[CrossRef] [SCOPUS Times Cited 2]




References Weight

Web of Science® Citations for all references: 2,330 TCR
SCOPUS® Citations for all references: 4,571 TCR

Web of Science® Average Citations per reference: 83 ACR
SCOPUS® Average Citations per reference: 163 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-03-17 08:52 in 188 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2019
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.




Website loading speed and performance optimization powered by: