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Outlier Generation and Anomaly Detection Based on Intelligent One-Class Techniques over a Bicomponent Mixing System

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14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) (SOCO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 950))

Abstract

One of the most important points to improve the profits in an industrial process lies on the fact of achieving a good optimisation and applying a smart maintenance plan. Under this circumstances an early anomaly plays an important role. Then, the implementation of classifiers for anomaly detection is an important challenge. As many of the anomalies that can occur in a plant have an unknown behaviour, it is necessary to generate artificial outliers to check these classifiers. This work presents different one-class intelligent techniques to perform anomaly detection in an industrial facility, used to obtain the main material for wind generator blades production. Furthermore, artificial anomaly data are generated to check the performance of each technique. The final results achieved are successful in general terms.

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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. 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 & Juan Albino Méndez-Pérez

Authors
  1. Esteban Jove
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  2. José-Luis Casteleiro-Roca
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  3. Héctor Quintián
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  4. Juan Albino Méndez-Pérez
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  5. José Luis Calvo-Rolle
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Corresponding author

Correspondence to Esteban Jove .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Jove, E., Casteleiro-Roca, JL., Quintián, H., Méndez-Pérez, J.A., Calvo-Rolle, J.L. (2020). Outlier Generation and Anomaly Detection Based on Intelligent One-Class Techniques over a Bicomponent Mixing System. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_38

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