|3/2019 - 6|
View TOC | « Previous Article | Next Article »
Spectral Subband Centroid Energy Vectors Algorithm and Artificial Neural Networks for Acoustic Emission Pattern ClassificationFLORENTINO, M. T. B. , Da COSTA, E. G. , FERREIRA, T. V. , GERMANO, A. D.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (837 KB) | Citation | Downloads: 651 | Views: 1,424|
acoustic emission, artificial neural networks, condition monitoring, corona, insulators
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
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|
| Gubanski, S. M. Dernfalk, A., Andersson J., Hillborg, H. "Diagnostic Methods for Outdoor Polymeric Insulators." IEEE Trans. Dielectrics and Electrical Insulation, vol. 14, n. 5, pp. 1065-1080, 2007. |
[CrossRef] [Web of Science Times Cited 126] [SCOPUS Times Cited 158]
 Cigre Working Group B2.21, "Assessment of in-service Composite Insulators by using Diagnostic Tools.", Electra, vol. 269, pp. 29-31, 2013.
 Al-Geelani, N. A., Piah, M. A. M., Bashir, N. "A Review on Hybrid Wavelet Regrouping Particle Swarm Optimization Neural Networks for Characterization of Partial Discharge Acoustic Signals." Renewable and Sustainable Energy Reviews, vol. 45, pp. 20-35, 2015.
 Herrera-Viedma, E., Lopez-Herrera, A. G. "A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling," Int. Journal of Computational Intelligence Systems, vol. 3, n. 4, pp. 420-437, 2010.
[CrossRef] [SCOPUS Times Cited 57]
 Pozna, C., Precup, R., Tar, J. K., Skrjanc, I., Preitl, S. "New results in modelling derived from Bayesian filtering," Knowledge-Based Systems, vol. 23, n. 2, 2010, pp. 182-194.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 63]
 Takacs, A., Kovacs, L., Rudas, I. J., Precup, R., Haidegger, T. "Models for Force Control in Telesurgical Robot Systems," Acta Polytechnica Hungarica, vol. 12, n. 8, 2015, pp. 95-114.
 Ruiz-Rangel, J., Hernandez, C. J. A., Gonzalez, L. M., Molinares, D. J. "ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory," Int. Journal of Artificial Intelligence, vol. 16, n. 1, 2018, pp 214-253.
 Gorur, R. S., Cherney, E. A., Burnham, J. T. Outdoor insulators, 1st ed. Phoenix: Ravi S. Gorur Inc., 1999.
 Vosloo, W. L., Macey, R. E., Tourreil, C. The Practical Guide to High Voltage Insulators. South Africa: Crown Publications cc, vol. 3, pp. 220, 2006.
 Ramirez, C., Moore, P. J. "Identification of surface discharges over new and aged polymeric chain insulators using a non invasive method", In: IEEE Proc. 41st Int. Universities Power Eng. Conf., 2006. pp. 903-906.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]
 Ferreira, T. V., Germano, A. D., Costa, E. G. "Ultrasound and Artificial Intelligence Applied to the Pollution Estimation in Insulations." IEEE Trans. Power Delivery, vol. 12, pp. 583-589, 2012.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 19]
 Menon. R., Kolambekar, S., Buch, N. J., Ramamoorty, M. "Correlation of acoustic emission method and electrical method for detection of partial discharges in transformers," in Proc. IEEE 7th Int. Conf. Solid Dielectrics, pp. 299-302, Jun. 2001.
[CrossRef] [Web of Science Times Cited 9]
 Muniraj, C., Chandrasekar, S. "Condition Monitoring of Outdoor Polymeric Insulators Using Wavelets and ANFIS", In: IEEE Int. Conf. on Power and Energy, Kuala Lumpur, 2010, pp. 346-351.
[CrossRef] [SCOPUS Times Cited 2]
 Nyamupangedengu, C., Luhlanga, L. P., Letlape T. "Acoustic and HF Detection of Defects on Porcelain Pin Insulators", In: IEEE Power Eng. Society Conf. and Expo. in Africa, Johannesburg, 2007.
[CrossRef] [SCOPUS Times Cited 11]
 Shurrab, I. Y., El-Hag, A., Assaleh, K., Ghunem, R. "Partial Discharge On-Line Monitoring of Outdoor Insulators", In: IEEE Int. Symp. on Electrical Insulation, San Juan, 2012, pp. 391-394.
[CrossRef] [SCOPUS Times Cited 13]
 Gorur, R. S., Chang, J. W., Amburgey, O. G. "Surface hydrophobicity of polymers used for outdoor insulation", IEEE Trans. Power Delivery, vol. 5, n. 4, pp. 1923-1933, 1990.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 85]
 Huang, C. M., Huang, Y. C. "A novel approach to real-time economic emission power dispatch", IEEE Trans. Power Systems, vol. 18, n. 1, 2003, pp. 288-294,
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 53]
 Kreuger, F. H., Gulski, E., Krivda, A. "Classification of partial discharges", IEEE Trans. Electrical Insulation, vol. 28, n. 6, 1993. pp. 917-931.
[CrossRef] [Web of Science Times Cited 208] [SCOPUS Times Cited 275]
 Ferreira, T. V., Germano, A. D., Silva, K. M., Costa, E. G. "Ultra-sound and Artificial Intelligence Applied to the Diagnosis of Insulations in the Field." High Voltage Engineering, vol. 38, n. 8, pp. 20061-20066, 2012.
 Harrold, R. T. "Acoustic Waveguides for Sensing and Locating Electrical Discharges in High Voltage Power Transformers and other Apparatus." IEEE Trans. Power Apparatus and Systems, vol. 98, n. 2, pp. 449-457, 1979.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 24]
 Lundgaard, L. E. "Partial Discharge XIII: acoustic partial discharge detection-fundamental considerations." IEEE Electrical Insulation Magazine, vol. 8, pp. 25-31, 1992.
[CrossRef] [SCOPUS Times Cited 245]
 Abdel-Salam, M., Abdel-Sattar, S., Sayed, Y., Ghally, M. "Early Detection of Weak Point in MEEC Distribution System." In: Industry Applications Conf. Record of the 2001 IEEE, 2001, Chicago. vol. 4, pp. 2541-2545.
[CrossRef] [SCOPUS Times Cited 5]
 Rocha, P. H. V., Fontgalland, G. "Measuring the radiation bands of overhead power lines glass insulators". Proc. of the IEEE 2014 Int. Conf. Antenna Measurements & Applications. France, 2014.
[CrossRef] [SCOPUS Times Cited 4]
 Dawson, G. A., Richards, C. N., Krider, E. P., Uman, M. A. "The Acoustic Output of a Long Spark". Journal of Geophysical Research, vol. 73, pp. 815-816, 1968.
[CrossRef] [Web of Science Times Cited 21]
 Harrold, R. T. "Acoustical Technology Applications in Electrical Insulation and Dielectrics." IEEE Trans. Electrical Insulation, vol. 20, n. 1, pp. 3-19, 1985.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 46]
 Gajic, B., Paliwal, K. K. "Speech Parametrization for Automatic Speech Recognition in Noisy Conditions," in: Proc. Norwegian Symp. Signal Processing, Trondheim, 2001.
 Paliwal, K. K. "Spectral Subband Centroid Features for Speech Recognition," in: Int. Conf. Acoustics, Speech and Signal Processing, Seattle, vol. 2, pp. 617-620, 1998.
[CrossRef] [SCOPUS Times Cited 138]
 McCulloch, W. S., Pitts, W. "A Logical Calculus of the Ideas Immanent in Nervous Activity." Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133, 1943.
[CrossRef] [SCOPUS Times Cited 10217]
 Haykin, S. O. Neural Networks and Learning Machines. 3. ed. New Jersey: Pearson Prentice Hall, 2008.
 Rosenblatt, F. "The Perceptron: A probabilistic model for information storage and organization in the brain," Psychological Review, vol. 65, pp. 386-408.
[CrossRef] [Web of Science Times Cited 4302] [SCOPUS Times Cited 5604]
 Riedmiller, M., Braun, H. "RPROP - A Fast Adaptive Learning Algorithm", In: Int. Symp. Computer and Information Science, 1993.
 Hagan, M. T, Menhaj, M. B. "Training Feedforward Networks with the Marquardt Algorithm," IEEE Trans. Neural Networks, vol. 5, pp. 989-993, 1994.
[CrossRef] [Web of Science Times Cited 5150] [SCOPUS Times Cited 6296]
 Bishop, C. M. Neural Networks for Pattern Recognition. Clarendon Press, Oxford. 1995.
 Kalman, B. L., Kwasny, S. C. "Why tanh: choosing a sigmoidal function." In: Int. Joint Conf. Neural Networks, 1992, Baltimore. vol. 2, pp. 578 - 581.
Web of Science® Citations for all references: 10,069 TCR
SCOPUS® Citations for all references: 23,321 TCR
Web of Science® Average Citations per reference: 288 ACR
SCOPUS® Average Citations per reference: 666 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 2023-06-03 02:34 in 149 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.
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.