Técnicas de clasificación supervisadas para la detección de anomalías en el control de procesos industriales
- Michelena, Álvaro 1
- Zayas-Gato, Francisco 1
- Jove, Esteban 1
- Casteleiro-Roca, José-Luis 1
- Quintián, Héctor 1
- Prieto Fernández, Natalia 1
- Alaiz Moretón, Héctor 2
- Calvo-Rolle, José Luis 1
-
1
Universidade da Coruña
info
-
2
Universidad de León
info
- Carlos Balaguer Bernaldo de Quirós (coord.)
- José Manuel Andújar Márquez (coord.)
- Ramon Costa Castelló (coord.)
- Carlos Ocampo Martínez (coord.)
- Jesús Fernández Lozano (coord.)
- Matilde Santos Peñas (coord.)
- José Enrique Simó Ten (coord.)
- Montserrat Gil Martínez (coord.)
- Jose Luis Calvo Rolle (coord.)
- Raúl Marín Prades (coord.)
- Eduardo Rocón de Lima (coord.)
- Elisabet Estévez Estévez (coord.)
- Pedro Jesús Cabrera Santana (coord.)
- David Muñoz de la Peña Sequedo (coord.)
- José Luis Guzmán Sánchez (coord.)
- José Luis Pitarch Pérez (coord.)
- Oscar Reinoso García (coord.)
- Oscar Déniz Suárez (coord.)
- Emilio Jiménez Macías (coord.)
- Vanesa Loureiro Vázquez (coord.)
Publisher: Servizo de Publicacións ; Universidade da Coruña
ISBN: 978-84-9749-841-8
Year of publication: 2022
Pages: 224-232
Congress: Jornadas de Automática (43. 2022. Logroño)
Type: Conference paper
Abstract
Nowadays, detecting anomalies in industrial processes is key to optimizing them and generating greater efficiency in the production process, bringing more significant benefits to companies. Therefore, in this paper, five supervised classification techniques are implemented to detect anomalies in industrial systems. These techniques have been trained and validated using a dataset that included labeled normal and anomalous operation data from a liquid level control plant. Finally, the results obtained were analyzed and compared to obtain the model with the best performance.