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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
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Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
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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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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
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2022-Jun-16
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  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
 
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Download PDF pdficon (8,079 KB) | Citation | Downloads: 1,047 | Views: 3,791

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

References keywords
induction(17), fault(13), diagnosis(9), motors(8), detection(8), motor(7), analysis(7), wavelet(6), vibration(5), mining(5)
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
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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

Cited-By Clarivate Web of Science

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Cited-By CrossRef

[1] Weakly Supervised Multilayer Perceptron for Industrial Fault Classification With Inaccurate and Incomplete Labels, Liao, Sifen, Jiang, Xiaoyu, Ge, Zhiqiang, IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, Issue 2, Volume 19, 2022.
Digital Object Identifier: 10.1109/TASE.2020.3043531
[CrossRef]

[2] Stator Current Based Multi-Class Fault Diagnosis of Three Phase Induction Motor using Machine Learning Framework, Maulik, Saubhik, Konar, Pratyay, Chattopadhyay, Paramita, 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), ISBN 978-1-6654-7380-4, 2022.
Digital Object Identifier: 10.1109/CATCON56237.2022.10077708
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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