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...

Descripción completa

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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784711/
https://www.ncbi.nlm.nih.gov/pubmed/36560107
http://dx.doi.org/10.3390/s22249738

Ejemplares similares