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Appl. Sci. 2019, 9(8), 1523; https://doi.org/10.3390/app9081523

Using ANN and SVM for the Detection of Acoustic Emission Signals Accompanying Epoxy Resin Electrical Treeing

1
Faculty of Electrical Engineering, Poznań University of Technology, Piotrowo 3A str., 60-965 Poznań, Poland
2
Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, Al. prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland
*
Author to whom correspondence should be addressed.
Received: 26 February 2019 / Revised: 25 March 2019 / Accepted: 8 April 2019 / Published: 12 April 2019
(This article belongs to the Special Issue Modelling, Simulation and Data Analysis in Acoustical Problems)
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Abstract

Electrical treeing is one of the effects of partial discharges in the solid insulation of high-voltage electrical insulating systems. The process involves the formation of conductive channels inside the dielectric. Acoustic emission (AE) is a method of partial discharge detection and measurement, which belongs to the group of non-destructive methods. If electrical treeing is detected, the measurement, recording, and analysis of signals, which accompany the phenomenon, become difficult due to the low signal-to-noise ratio and possible multiple signal reflections from the boundaries of the object. That is why only selected signal parameters are used for the detection and analysis of the phenomenon. A detailed analysis of various acoustic emission signals is a complex and time-consuming process. It has inspired the search for new methods of identifying the symptoms related to partial discharge in the recorded signal. Bearing in mind that a similar signal is searched, denoting a signal with similar characteristics, the use of artificial neural networks seems pertinent. The paper presents an effort to automate the process of insulation material condition identification based on neural classifiers. An attempt was made to develop a neural classifier that enables the detection of the symptoms in the recorded acoustic emission signals, which are evidence of treeing. The performed studies assessed the efficiency with which different artificial neural networks (ANN) are able to detect treeing-related signals and the appropriate selection of such input parameters as statistical indicators or analysis windows. The feedforward network revealed the highest classification efficiency among all analyzed networks. Moreover, the use of primary component analysis helps to reduce the teaching data to one variable at a classification efficiency of up to 1%. View Full-Text
Keywords: solid dielectrics; acoustic emission; artificial neural networks; electrical treeing; wavelets; non-destructive testing; high-voltage insulating systems solid dielectrics; acoustic emission; artificial neural networks; electrical treeing; wavelets; non-destructive testing; high-voltage insulating systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dobrzycki, A.; Mikulski, S.; Opydo, W. Using ANN and SVM for the Detection of Acoustic Emission Signals Accompanying Epoxy Resin Electrical Treeing. Appl. Sci. 2019, 9, 1523.

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