Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/77995
Título: | Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications |
Autor(es): | Putnik, Goran D. Manupati, Vijaya Kumar Pabba, Sai Krishna Varela, M.L.R. Ferreira, Francisco |
Palavras-chave: | Manufacturing system Predictive maintenance Maintenance |
Data: | 2021 |
Editora: | Elsevier 1 |
Revista: | CIRP Annals - Manufacturing Technology |
Citação: | Putnik, G. D., Manupati, V. K., Pabba, S. K., Varela, L., & Ferreira, F. (2021). Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications. CIRP Annals, 70(1), 365-368. doi: https://doi.org/10.1016/j.cirp.2021.04.046 |
Resumo(s): | The paper presents two original and innovative contributions: 1) the model of machine learning (ML) based approach for predictive maintenance in manufacturing system based on machine status indications only, and 2) semi-Double-loop machine learning based intelligent Cyber-Physical System (I-CPS) architecture as a higher-level environment for ML based predictive maintenance execution. Considering only the machine status information provides rapid and very low investment-based implementation of an advanced predictive maintenance paradigm, especially important for SMEs. The model is validated in real-life situations, exploring different learning algorithms and strategies for learning maintenance predictive models. The findings show very high level of prediction accuracy. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/77995 |
DOI: | 10.1016/j.cirp.2021.04.046 |
ISSN: | 0007-8506 |
Versão da editora: | https://www.sciencedirect.com/science/article/pii/S0007850621000706 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CGIT - Artigos em revistas de circulação internacional com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
1-s2.0-S0007850621000706-main.pdf | CirpAnnals_2021 | 1,23 MB | Adobe PDF | Ver/Abrir |