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

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


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  3/2019 - 6

Spectral Subband Centroid Energy Vectors Algorithm and Artificial Neural Networks for Acoustic Emission Pattern Classification

FLORENTINO, M. T. B. See more information about FLORENTINO, M. T. B. on SCOPUS See more information about FLORENTINO, M. T. B. on IEEExplore See more information about FLORENTINO, M. T. B. on Web of Science, Da COSTA, E. G. See more information about  Da COSTA, E. G. on SCOPUS See more information about  Da COSTA, E. G. on SCOPUS See more information about Da COSTA, E. G. on Web of Science, FERREIRA, T. V. See more information about  FERREIRA, T. V. on SCOPUS See more information about  FERREIRA, T. V. on SCOPUS See more information about FERREIRA, T. V. on Web of Science, GERMANO, A. D. See more information about GERMANO, A. D. on SCOPUS See more information about GERMANO, A. D. on SCOPUS See more information about GERMANO, A. D. on Web of Science
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Author keywords
acoustic emission, artificial neural networks, condition monitoring, corona, insulators

References keywords
power(10), insulators(9), networks(7), insulation(7), systems(6), partial(6), neural(6), acoustic(6), outdoor(5), speech(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 49 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03006
Web of Science Accession Number: 000486574100006
SCOPUS ID: 85072171267

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This work proposes and evaluates a methodology for monitoring and diagnosis of polymeric insulators in operation based on the parameterization of acoustic emissions (AE) created by corona and electrical surface discharges. The parameterization was performed with the use of the spectral subband centroid energy vectors (SSCEV) algorithm, which compresses the frequency spectrum and presents the results of the AE energies in several frequency bands. Thus, it was possible to calculate the dominant acoustic emission frequencies. This parameter was used as reference for an operating point of the insulators and, therefore, it was used to classify them. This classification was correlated to the classification obtained by visual inspection in the laboratory, where the insulators were divided into three distinct classes: clean, polluted and damaged. Aiming to insert an aid to the decision-making, this work still proposes the use of artificial neural networks (ANN) for pattern recognition. In this way, we performed a sensitivity analysis of the parameters that influence the SSCEV and ANN, in order to obtain the values and configurations with higher performance. The use of Levenberg-Marquardt training algorithm has proved to be more suitable, since it showed hit rates and convergence up to 97.66 percent and 70 epochs, respectively.

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

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References Weight

Web of Science® Citations for all references: 5,230 TCR
SCOPUS® Citations for all references: 19,235 TCR

Web of Science® Average Citations per reference: 149 ACR
SCOPUS® Average Citations per reference: 550 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 2021-09-15 13:15 in 163 seconds.

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