Data driven fault detection in hydroelectric power plants based on deep neural networks (2022)
- Authors:
- USP affiliated authors: SOUZA, GILBERTO FRANCISCO MARTHA DE - EP ; ROSA, TIAGO GASPAR DA - EP ; MELANI, ARTHUR HENRIQUE DE ANDRADE - EP ; MICHALSKI, MIGUEL ANGELO DE CARVALHO - EP
- Unidade: EP
- DOI: 10.3850/978-981-18-5183-4_R22-05-074-cd
- Subjects: MANUTENÇÃO PREDITIVA; USINAS HIDRELÉTRICAS; FALHA; SIMULAÇÃO; REDES NEURAIS
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: European Safety and Reliability Conference
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
ROSA, Tiago Gaspar da et al. Data driven fault detection in hydroelectric power plants based on deep neural networks. 2022, Anais.. Singapore: Escola Politécnica, Universidade de São Paulo, 2022. Disponível em: https://www.rpsonline.com.sg/proceedings/esrel2022/html/R22-05-074.xml. Acesso em: 27 jan. 2026. -
APA
Rosa, T. G. da, Melani, A. H. de A., Kashiwagi, F. N., Michalski, M. A. D. C., Souza, G. F. M. de, Salles, G. M. de O., & Rigoni, E. (2022). Data driven fault detection in hydroelectric power plants based on deep neural networks. In Proceedings. Singapore: Escola Politécnica, Universidade de São Paulo. doi:10.3850/978-981-18-5183-4_R22-05-074-cd -
NLM
Rosa TG da, Melani AH de A, Kashiwagi FN, Michalski MADC, Souza GFM de, Salles GM de O, Rigoni E. Data driven fault detection in hydroelectric power plants based on deep neural networks [Internet]. Proceedings. 2022 ;[citado 2026 jan. 27 ] Available from: https://www.rpsonline.com.sg/proceedings/esrel2022/html/R22-05-074.xml -
Vancouver
Rosa TG da, Melani AH de A, Kashiwagi FN, Michalski MADC, Souza GFM de, Salles GM de O, Rigoni E. Data driven fault detection in hydroelectric power plants based on deep neural networks [Internet]. Proceedings. 2022 ;[citado 2026 jan. 27 ] Available from: https://www.rpsonline.com.sg/proceedings/esrel2022/html/R22-05-074.xml - Plant prioritization for updating maintenance policies: a power sector case study
- Optimizing preventive maintenance policies: a hydroelectric power plant case study
- Semi-supervised framework with autoencoder-based neural networks for fault prognosis
- Imperfect preventive maintenance optimization with variable age reduction factor and independent intervention level
- Maintenance management optimization to improve system availability based on stochastic block diagram
- Applying cluster analysis to support failure management policy selection in asset management: a hydropower plant case study
- Applying an unsupervised machine learning method for defining maintenance significant items
- Applying principal component analysis for multi-parameter failure prognosis and determination of remaining useful life
- Identifying changes in degradation stages for an unsupervised fault prognosis method for engineering systems
- Remaining useful life estimation based on an adaptive approach of Autoregressive Integrated Moving Average (ARIMA)
Informações sobre o DOI: 10.3850/978-981-18-5183-4_R22-05-074-cd (Fonte: oaDOI API)
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