Anomaly Detection Over an Ultrasonic Sensor in an Industrial Plant

  1. Esteban Jove 12
  2. José-Luis Casteleiro-Roca 1
  3. Jose Manuel González-Cava 2
  4. Héctor Quintián 1
  5. Héctor Alaiz-Moretón 3
  6. Bruno Baruque 4
  7. Méndez-Pérez, Juan Albino 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

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

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 492-503

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

Tipo: Aportación congreso

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Resumen

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