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:
Research
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

Internet

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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