Anomaly Detection Over an Ultrasonic Sensor in an Industrial Plant

  1. Esteban Jove Pérez 12
  2. José-Luis Casteleiro-Roca 1
  3. José Manuel González Cava 2
  4. Héctor Quintián Pardo 1
  5. Héctor Alaiz Moretón 3
  6. Bruno Baruque Zanón 4
  7. Juan Albino Méndez Pérez 2
  8. José Luis Calvo Rolle 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  2. 2 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

  3. 3 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  4. 4 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Mostra les afiliacions +
Llibre:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (ed. lit.)
  2. Lidia Sánchez González (ed. lit.)
  3. Manuel Castejón Limas (ed. lit.)
  4. Héctor Quintián Pardo (ed. lit.)
  5. Emilio Santiago Corchado Rodríguez (ed. lit.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3

Any de publicació: 2019

Pàgines: 492-503

Congrés: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipus: Aportació congrés

DIALNET GOOGLE SCHOLAR

Resum

The significant industrial developments in terms of digitalization and optimization, have focused the attention on anomaly detection techniques. This work presents a detailed study about the performance of different one-class intelligent techniques, used for detecting anomalies in the performance of an ultrasonic sensor. The initial dataset is obtained from a control level plant, and different percentage variations in the sensor measurements are generated. For each variation, the performance of three one-class classifiers are assessed, obtaining very good results.

Fuente de los datos: Dialnet