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

  • Esteban JoveEmail author
  • José-Luis Casteleiro-Roca
  • Héctor Quintián
  • Juan Albino Méndez-Pérez
  • José Luis Calvo-Rolle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Anomaly detection Control system Outlier generation 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Esteban Jove
    • 1
    • 2
    Email author
  • José-Luis Casteleiro-Roca
    • 1
  • Héctor Quintián
    • 1
  • Juan Albino Méndez-Pérez
    • 2
  • José Luis Calvo-Rolle
    • 1
  1. 1.Department of Industrial EngineeringUniversity of A CoruñaFerrol, A CoruñaSpain
  2. 2.Department of Computer Science and System EngineeringUniversidad de La LagunaS/C de TenerifeSpain

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