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Anomaly Detection Over an Ultrasonic Sensor in an Industrial Plant

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Hybrid Artificial Intelligent Systems (HAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of A Coruña, Avda. 19 de febrero s/n, 15405, Ferrol, A Coruña, Spain

    Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián & José Luis Calvo-Rolle

  2. Departamento de Ingeniería Eléctrica y de Sistemas y Automática, Universidad de León, Edificio Tecnológico - Campus de Vegazana s/n, 24071, León, Spain

    Héctor Alaiz-Moretón

  3. Departamento de Ingeniería Civil, Universidad de Burgos, Calle Francisco de Vitoria, s/n, 09006, Burgos, Spain

    Bruno Baruque

  4. Department of Computer Science and System Engineering, Universidad de La Laguna, Avda. Astrof. Francisco Sánchez s/n, 38200, S/C de Tenerife, Spain

    Esteban Jove, Jose Manuel González-Cava & Juan Albino Méndez-Pérez

Authors
  1. Esteban Jove
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  2. José-Luis Casteleiro-Roca
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  3. Jose Manuel González-Cava
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  4. Héctor Quintián
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  5. Héctor Alaiz-Moretón
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  6. Bruno Baruque
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  7. Juan Albino Méndez-Pérez
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  8. José Luis Calvo-Rolle
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Corresponding author

Correspondence to Esteban Jove .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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Jove, E. et al. (2019). Anomaly Detection Over an Ultrasonic Sensor in an Industrial Plant. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_42

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_42

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