Fault detection and diagnosis based on unsupervised machine learning methods: a Kaplan turbine case study (2022)
- Authors:
- USP affiliated authors: MELANI, ARTHUR HENRIQUE DE ANDRADE - EP ; SOUZA, GILBERTO FRANCISCO MARTHA DE - EP ; MICHALSKI, MIGUEL ANGELO DE CARVALHO - EP ; SILVA, RENAN FAVARÃO DA - EP
- Unidade: EP
- DOI: 10.3390/en15010080
- Subjects: MANUTENÇÃO PREDITIVA; TURBINAS HIDRÁULICAS; FALHA; APRENDIZADO COMPUTACIONAL
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MICHALSKI, Miguel Angelo De Carvalho et al. Fault detection and diagnosis based on unsupervised machine learning methods: a Kaplan turbine case study. Energies, v. 15, n. 11, p. 1-20, 2022Tradução . . Disponível em: https://doi.org/10.3390/en15010080. Acesso em: 22 jan. 2026. -
APA
Michalski, M. A. D. C., Melani, A. H. de A., Silva, R. F. da, Souza, G. F. M. de, & Hamaji, F. H. (2022). Fault detection and diagnosis based on unsupervised machine learning methods: a Kaplan turbine case study. Energies, 15( 11), 1-20. doi:10.3390/en15010080 -
NLM
Michalski MADC, Melani AH de A, Silva RF da, Souza GFM de, Hamaji FH. Fault detection and diagnosis based on unsupervised machine learning methods: a Kaplan turbine case study [Internet]. Energies. 2022 ; 15( 11): 1-20.[citado 2026 jan. 22 ] Available from: https://doi.org/10.3390/en15010080 -
Vancouver
Michalski MADC, Melani AH de A, Silva RF da, Souza GFM de, Hamaji FH. Fault detection and diagnosis based on unsupervised machine learning methods: a Kaplan turbine case study [Internet]. Energies. 2022 ; 15( 11): 1-20.[citado 2026 jan. 22 ] Available from: https://doi.org/10.3390/en15010080 - A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network
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Informações sobre o DOI: 10.3390/en15010080 (Fonte: oaDOI API)
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