Técnicas de clasificación supervisadas para la detección de anomalías en el control de procesos industriales

  1. Michelena, Álvaro 1
  2. Zayas-Gato, Francisco 1
  3. Jove, Esteban 1
  4. Casteleiro-Roca, José-Luis 1
  5. Quintián, Héctor 1
  6. Prieto Fernández, Natalia 1
  7. Alaiz Moretón, Héctor 2
  8. Calvo-Rolle, José Luis 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  2. 2 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Book:
XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)
  1. Carlos Balaguer Bernaldo de Quirós (coord.)
  2. José Manuel Andújar Márquez (coord.)
  3. Ramon Costa Castelló (coord.)
  4. Carlos Ocampo Martínez (coord.)
  5. Jesús Fernández Lozano (coord.)
  6. Matilde Santos Peñas (coord.)
  7. José Enrique Simó Ten (coord.)
  8. Montserrat Gil Martínez (coord.)
  9. Jose Luis Calvo Rolle (coord.)
  10. Raúl Marín Prades (coord.)
  11. Eduardo Rocón de Lima (coord.)
  12. Elisabet Estévez Estévez (coord.)
  13. Pedro Jesús Cabrera Santana (coord.)
  14. David Muñoz de la Peña Sequedo (coord.)
  15. José Luis Guzmán Sánchez (coord.)
  16. José Luis Pitarch Pérez (coord.)
  17. Oscar Reinoso García (coord.)
  18. Oscar Déniz Suárez (coord.)
  19. Emilio Jiménez Macías (coord.)
  20. 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

DIALNET GOOGLE SCHOLAR lock_openRUC editor

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.

Data source: Dialnet