Semi-supervised framework with autoencoder-based neural networks for fault prognosis (2022)
- Authors:
- USP affiliated authors: MELANI, ARTHUR HENRIQUE DE ANDRADE - EP ; SOUZA, GILBERTO FRANCISCO MARTHA DE - EP ; ROSA, TIAGO GASPAR DA - EP ; KASHIWAGI, FABIO NORIKAZU - EP
- Unidade: EP
- DOI: 10.3390/s22249738
- Subjects: FALHA; APRENDIZAGEM PROFUNDA; REDES NEURAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
ROSA, Tiago Gaspar da et al. Semi-supervised framework with autoencoder-based neural networks for fault prognosis. Sensors, v. 22, n. 24, p. 1-23, 2022Tradução . . Disponível em: https://doi.org/10.3390/s22249738. Acesso em: 26 jan. 2026. -
APA
Rosa, T. G. da, Melani, A. H. de A., Pereira, F. H., Kashiwagi, F. N., Souza, G. F. M. de, & Salles, G. M. de O. (2022). Semi-supervised framework with autoencoder-based neural networks for fault prognosis. Sensors, 22( 24), 1-23. doi:10.3390/s22249738 -
NLM
Rosa TG da, Melani AH de A, Pereira FH, Kashiwagi FN, Souza GFM de, Salles GM de O. Semi-supervised framework with autoencoder-based neural networks for fault prognosis [Internet]. Sensors. 2022 ; 22( 24): 1-23.[citado 2026 jan. 26 ] Available from: https://doi.org/10.3390/s22249738 -
Vancouver
Rosa TG da, Melani AH de A, Pereira FH, Kashiwagi FN, Souza GFM de, Salles GM de O. Semi-supervised framework with autoencoder-based neural networks for fault prognosis [Internet]. Sensors. 2022 ; 22( 24): 1-23.[citado 2026 jan. 26 ] Available from: https://doi.org/10.3390/s22249738 - Optimizing preventive maintenance policies: a hydroelectric power plant case study
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Informações sobre o DOI: 10.3390/s22249738 (Fonte: oaDOI API)
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