Comparative Study of One-Class Based Anomaly Detection Techniques for a Bicomponent Mixing Machine Monitoring. Issue 7 (2nd October 2020)

• Record Type: Journal Article
• Authors: Jove, Esteban; Casteleiro-Roca, José-Luis; Casado-Vara, Roberto; Quintián, Héctor; Pérez, Juan Albino Méndez; Mohamad, Mohd Saberi; Luis Calvo-Rolle, José
• Is Part Of: Cybernetics and systems. Volume 51:Issue 7(2020)
• Page Start: 649
• Page End: 667
• Publication Date: 2020-10-02
• Subjects: Anomaly detection -- control system -- one-class -- outlier generation; Cybernetics -- Periodicals; System theory -- Periodicals; 003.5
• DOI: 10.1080/01969722.2020.1798641
• Languages: English
• ISSNs: 0196-9722

Abstract

Abstract: 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.