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Transformer fault diagnosis using continuous sparse autoencoder

This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recogni...

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Detalles Bibliográficos
Autores principales: Wang, Lukun, Zhao, Xiaoying, Pei, Jiangnan, Tang, Gongyou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830783/
https://www.ncbi.nlm.nih.gov/pubmed/27119052
http://dx.doi.org/10.1186/s40064-016-2107-7
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author Wang, Lukun
Zhao, Xiaoying
Pei, Jiangnan
Tang, Gongyou
author_facet Wang, Lukun
Zhao, Xiaoying
Pei, Jiangnan
Tang, Gongyou
author_sort Wang, Lukun
collection PubMed
description This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.
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spelling pubmed-48307832016-04-26 Transformer fault diagnosis using continuous sparse autoencoder Wang, Lukun Zhao, Xiaoying Pei, Jiangnan Tang, Gongyou Springerplus Research This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis. Springer International Publishing 2016-04-14 /pmc/articles/PMC4830783/ /pubmed/27119052 http://dx.doi.org/10.1186/s40064-016-2107-7 Text en © Wang et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Wang, Lukun
Zhao, Xiaoying
Pei, Jiangnan
Tang, Gongyou
Transformer fault diagnosis using continuous sparse autoencoder
title Transformer fault diagnosis using continuous sparse autoencoder
title_full Transformer fault diagnosis using continuous sparse autoencoder
title_fullStr Transformer fault diagnosis using continuous sparse autoencoder
title_full_unstemmed Transformer fault diagnosis using continuous sparse autoencoder
title_short Transformer fault diagnosis using continuous sparse autoencoder
title_sort transformer fault diagnosis using continuous sparse autoencoder
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830783/
https://www.ncbi.nlm.nih.gov/pubmed/27119052
http://dx.doi.org/10.1186/s40064-016-2107-7
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AT peijiangnan transformerfaultdiagnosisusingcontinuoussparseautoencoder
AT tanggongyou transformerfaultdiagnosisusingcontinuoussparseautoencoder