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Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an in...

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Detalles Bibliográficos
Autores principales:	da Rosa, Tiago Gaspar, Melani, Arthur Henrique de Andrade, Pereira, Fabio Henrique, Kashiwagi, Fabio Norikazu, de Souza, Gilberto Francisco Martha, Salles, Gisele Maria De Oliveira
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784711/ https://www.ncbi.nlm.nih.gov/pubmed/36560107 http://dx.doi.org/10.3390/s22249738

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