A hybrid one‐class approach for detecting anomalies in industrial systems. Issue 9 (8th March 2022)

• Record Type: Journal Article
• Authors: Zayas‐Gato, Francisco; Jove, Esteban; Casteleiro‐Roca, José‐Luis; Quintián, Héctor; Piñón‐Pazos, Andrés; Simić, Dragan; Calvo‐Rolle, José Luis
• Other Names: Herrero Álvaro guestEditor.; Urda Daniel guestEditor.; Sedano Javier guestEditor.; Quintián Héctor guestEditor.; Corchado Emilio guestEditor.; Ahmed Syed Hassan guestEditor.; Khan Murad guestEditor.; Guibene Wael guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-08
• Subjects: anomaly detection -- clustering -- industrial system -- one‐class -- optimization; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12990
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The significant advance of Internet of Things in industrial environments has provided the possibility of monitoring the different variables that come into play in an industrial process. This circumstance allows the supervision of the current state of an industrial plant and the consequent decision making possibilities. Then, the use of anomaly detection techniques are presented as a powerful tool to determine unexpected situations. The present research is based on the implementation of one‐class classifiers to detect anomalies in two industrial systems. The proposal is validated using two real datasets registered during different operating points of two industrial plants. To ensure a better performance, a clustering process is developed prior the classifier implementation. Then, local classifiers are trained over each cluster, leading to successful results when they are tested with both real and artificial anomalies. Validation results present in all cases, AUC values above 90%.