Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/79438| Título: | A comparative study to detect flowmeter deviations using one-class classifiers |
| Autor(es): | Jove, Esteban Casteleiro-Roca, José Luis Quintián, Héctor Zayas-Gato, Francisco Novais, Paulo Méndez-Pérez, Juan Albino Calvo-Rolle, José Luis |
| Palavras-chave: | Anomaly detection Flowmeter One-class |
| Data: | 2021 |
| Editora: | Springer |
| Revista: | Advances in Intelligent Systems and Computing |
| Citação: | Jove, E. et al. (2021). A Comparative Study to Detect Flowmeter Deviations Using One-Class Classifiers. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020). CISIS 2019. Advances in Intelligent Systems and Computing, vol 1267. Springer, Cham. https://doi.org/10.1007/978-3-030-57805-3_7 |
| Resumo(s): | The use of bicomponent materials has encouraged the proliferation of wind turbine blades to produce electric power. However, the high complexity of the process followed to obtain this kind of materials difficult the problem of detecting anomalous situations in the plant, due to sensors or actuators malfunctions. This work analyses the use of different one-class techniques to detect deviations in one flowmeter located in a bicomponent mixing machine installation. In this case, a comparative analysis is carried out by modifying the percentage deviation of the sensor measurements. |
| Tipo: | Artigo em ata de conferência |
| URI: | https://hdl.handle.net/1822/79438 |
| ISBN: | 978-3-030-57804-6 |
| DOI: | 10.1007/978-3-030-57805-3_7 |
| ISSN: | 2194-5357 |
| Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-57805-3_7 |
| Arbitragem científica: | yes |
| Acesso: | Acesso restrito UMinho |
| Aparece nas coleções: |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| CISIS2020_paper_56.pdf Acesso restrito! | 360,29 kB | Adobe PDF | Ver/Abrir |
