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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: May 2024
Next issue: Aug 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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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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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
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 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 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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  1/2017 - 7

 HIGH-IMPACT PAPER 

Vibration Based Broken Bar Detection in Induction Machine for Low Load Conditions

MATIC, D. See more information about MATIC, D. on SCOPUS See more information about MATIC, D. on IEEExplore See more information about MATIC, D. on Web of Science, KANOVIC, Z. See more information about KANOVIC, Z. on SCOPUS See more information about KANOVIC, Z. on SCOPUS See more information about KANOVIC, Z. on Web of Science
 
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Download PDF pdficon (1,535 KB) | Citation | Downloads: 1,417 | Views: 2,917

Author keywords
motor, bar, vibration, fault, detection

References keywords
diagnosis(11), induction(10), rotor(8), detection(8), broken(8), fault(7), sanchez(6), pineda(6), motor(6), vibration(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 49 - 54
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01007
Web of Science Accession Number: 000396335900007
SCOPUS ID: 85014214831

Abstract
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A new method for broken bar detection, based on vibration signal analysis, is presented in this paper. While there are several methods for broken bar detection at low slip based on the current signal analysis, detection based on vibration signals attracts much less attention. In the current paper, detection of the broken bar was conducted by observing fault frequency content of the modulus of the analytical vibration signal. A broken bar feature is extracted from low frequency range even for low slip conditions. Although this method is successfully used for broken bar detection based on current signal analysis, it is important to verify the method when vibration signal is measured. Procedure is verified in a real industrial environment for induction motor of 3.15 MW.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 8 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 11
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Cited-By CrossRef

[1] Classification and Authentication of Induction Motor Faults using Time and Frequency Feature Dependent Probabilistic Neural Network Model, Thakur, Arunava Kabiraj, Mukherjee, Alok, Kundu, Palash Kumar, Das, Arabinda, Journal of The Institution of Engineers (India): Series B, ISSN 2250-2106, Issue 3, Volume 104, 2023.
Digital Object Identifier: 10.1007/s40031-023-00872-5
[CrossRef]

[2] Current Park’s Vector Pattern Technique for Diagnosis of Broken Rotor Bars Fault in Saturated Induction Motor, Abdellah, Chaouch, Mama, Chouitek, Meflah Abderrahmane, Mohamed Reda, Mohammed, Belaid, Journal of Electrical Engineering & Technology, ISSN 1975-0102, Issue 4, Volume 18, 2023.
Digital Object Identifier: 10.1007/s42835-022-01342-6
[CrossRef]

[3] Electrical Signature Analysis for Condition Monitoring of Permanent Magnet Synchronous Machine, SALOMON, C. P., FERREIRA, C., LAMBERT-TORRES, G., TEIXEIRA, C. E., BORGES DA SILVA, L. E., SANTANA, W. C., BONALDI, E. L., DE OLIVEIRA, L. E. L., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 18, 2018.
Digital Object Identifier: 10.4316/AECE.2018.04011
[CrossRef] [Full text]

[4] Evaluation and Classification of Double Bar Breakages Through Three-Axes Vibration Sensor in Induction Motors, Goktas, Taner, IEEE Sensors Journal, ISSN 1530-437X, Issue 13, Volume 22, 2022.
Digital Object Identifier: 10.1109/JSEN.2022.3176059
[CrossRef]

[5] Investigation on Electromagnetic Performance of Induction Motor with Rotor Bar Faults considering Motor Current Signals, PARK, Y.-S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.04005
[CrossRef] [Full text]

[6] Broken Rotor Bar Fault Detection in Asynchronous Machines Using Vibration Analysis, Treml, A. E., Flauzino, R. A., Ramos, R. A., Brito, G. C., 2019 IEEE Power & Energy Society General Meeting (PESGM), ISBN 978-1-7281-1981-6, 2019.
Digital Object Identifier: 10.1109/PESGM40551.2019.8973827
[CrossRef]

[7] EMD and MCSA Improved via Hilbert Transform Analysis on Asynchronous Machines for Broken Bar Detection Using Vibration Analysis, Treml, A. E., Flauzino, R. A., Brito, G. C., 2019 IEEE Milan PowerTech, ISBN 978-1-5386-4722-6, 2019.
Digital Object Identifier: 10.1109/PTC.2019.8810643
[CrossRef]

[8] Incipient Broken Rotor Bar Fault Diagnosis Based on Extended Prony Spectral Analysis Technique, Zhuzhi, Jia, Hongyu, Zhu, Xuyang, Liu, Hang, Shang, 2018 37th Chinese Control Conference (CCC), ISBN 978-988-15639-5-8, 2018.
Digital Object Identifier: 10.23919/ChiCC.2018.8483365
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
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