Comparative Study of One-Class Based Anomaly Detection Techniques for a Bicomponent Mixing Machine Monitoring

One critical point to improve the economic and technical results of every industrial process lies on the fact of achieving a good optimization, and applying a smart maintenance plan. In this context, the tools development for detecting the appearance of any kind of anomaly represents an important ch...

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Bibliographic Details
Published in:Cybernetics and systems 2020-10, Vol.51 (7), p.649-667
Main Authors: Jove, Esteban, Casteleiro-Roca, José-Luis, Casado-Vara, Roberto, Quintián, Héctor, Pérez, Juan Albino Méndez, Mohamad, Mohd Saberi, Luis Calvo-Rolle, José
Format: Article
Language:English
Subjects:
Anomaly detection
control system
one-class
outlier generation
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Summary:One critical point to improve the economic and technical results of every industrial process lies on the fact of achieving a good optimization, and applying a smart maintenance plan. In this context, the tools development for detecting the appearance of any kind of anomaly represents an important challenge. For this reason, the implementation of classifiers for anomaly detection tasks has been a significant trend in the scientific community. However, since the behavior of the potential anomalies that may occur in a plant is unknown, it is necessary to generate artificial outliers to assess these classifiers. This paper proposes the performance checking of different intelligent one-class techniques to detect anomalies in an industrial plant, used to obtain the main material for wind generator blades production. These classifiers are tested using anomaly data generated, giving successful results.
ISSN:0196-9722
1087-6553
DOI:10.1080/01969722.2020.1798641