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

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
Main Authors: Tiago Gaspar da Rosa, Arthur Henrique de Andrade Melani, Fabio Henrique Pereira, Fabio Norikazu Kashiwagi, Gilberto Francisco Martha de Souza, Gisele Maria De Oliveira Salles
Formato: Artigo
Idioma:English
Publicado: MDPI AG 2022-12-01
Series:Sensors
Subjects:
deep neural networks
fault prognosis
autoencoder
remaining useful life (RUL)
Acceso en liña:https://www.mdpi.com/1424-8220/22/24/9738

Internet

https://www.mdpi.com/1424-8220/22/24/9738

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