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Identification and extraction of surface discharge acoustic emission signals using wavelet neural network

A hybrid model incorporating wavelet and feed forward back propagation neural network (WFFB-NN) is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge (SD) activity and hence differentiate abnormal operating conditions from the normal ones. The...

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Main Authors: Al-geelani, Nasir Ahmed, Mohamed Piah, Mohamed Afendi
Format: Article
Published: International Academy Publishing (IAP) 2012
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/30514/
http://dx.doi.org/10.7763/IJCEE.2012.V4.536
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spelling my.utm.305142019-01-28T03:46:58Z http://eprints.utm.my/id/eprint/30514/ Identification and extraction of surface discharge acoustic emission signals using wavelet neural network Al-geelani, Nasir Ahmed Mohamed Piah, Mohamed Afendi TK Electrical engineering. Electronics Nuclear engineering A hybrid model incorporating wavelet and feed forward back propagation neural network (WFFB-NN) is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge (SD) activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of IEC 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for SD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension. Wavelet signal processing toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. The test results show that the proposed approach is efficient and reliable. The error during training process was acceptable and very low which attained 0.0074 in only 14 iterations. International Academy Publishing (IAP) 2012-08 Article PeerReviewed Al-geelani, Nasir Ahmed and Mohamed Piah, Mohamed Afendi (2012) Identification and extraction of surface discharge acoustic emission signals using wavelet neural network. International Journal of Computer and Electrical Engineering, 4 (4). pp. 471-474. ISSN 1793-8163 http://dx.doi.org/10.7763/IJCEE.2012.V4.536 DOI: 10.7763/IJCEE.2012.V4.536
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Al-geelani, Nasir Ahmed
Mohamed Piah, Mohamed Afendi
Identification and extraction of surface discharge acoustic emission signals using wavelet neural network
description A hybrid model incorporating wavelet and feed forward back propagation neural network (WFFB-NN) is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge (SD) activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of IEC 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for SD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension. Wavelet signal processing toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. The test results show that the proposed approach is efficient and reliable. The error during training process was acceptable and very low which attained 0.0074 in only 14 iterations.
format Article
author Al-geelani, Nasir Ahmed
Mohamed Piah, Mohamed Afendi
author_facet Al-geelani, Nasir Ahmed
Mohamed Piah, Mohamed Afendi
author_sort Al-geelani, Nasir Ahmed
title Identification and extraction of surface discharge acoustic emission signals using wavelet neural network
title_short Identification and extraction of surface discharge acoustic emission signals using wavelet neural network
title_full Identification and extraction of surface discharge acoustic emission signals using wavelet neural network
title_fullStr Identification and extraction of surface discharge acoustic emission signals using wavelet neural network
title_full_unstemmed Identification and extraction of surface discharge acoustic emission signals using wavelet neural network
title_sort identification and extraction of surface discharge acoustic emission signals using wavelet neural network
publisher International Academy Publishing (IAP)
publishDate 2012
url http://eprints.utm.my/id/eprint/30514/
http://dx.doi.org/10.7763/IJCEE.2012.V4.536
_version_ 1643648573182574592
score 13.211869

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