A Global Classifier Implementation for Detecting Anomalies by Using One-Class Techniques over a Laboratory Plant

  • 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 1004)


The energy and the product optimization of the industrial processes has played a key role during last decades. In this field, the appearance of any kind of anomaly may represent an important issue. Then, anomaly detection in an industrial plant is specially relevant.

In this work, the anomaly detection over level plant control is achieved, by using three one class intelligent techniques. Different global classifiers are trained and tested with real data from a laboratory plant, whose main aim is to control the tank liquid level. The results of each classifier are assessed and validated with real anomalies, leading to good results, in general terms.


Fault detection One-class ACH Autoencoder SVM 


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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 EngineeringUniversity of La LagunaS/C de TenerifeSpain

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