A hybrid one-class approach for detecting anomalies in industrial systems(Article)(Open Access)
- aCTC, Department of Industrial Engineering, CITIC, University of A Coruña, A Coruña, Spain
- bFaculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
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%. © 2022 The Authors. Expert Systems published by John Wiley & Sons Ltd.
Indexed keywords
| Engineering controlled terms: | Clustering algorithmsDecision makingExpert systemsIndustrial plantsIndustrial research |
|---|---|
| Engineering uncontrolled terms | 'currentAnomaly detectionClusteringsDecisions makingsIndustrial environmentsIndustrial processsIndustrial systemsOne-classOne-class classifierOptimisations |
| Engineering main heading: | Anomaly detection |
Funding details
| Funding sponsor | Funding number | Acronym |
|---|---|---|
| ED431G 2019/01 | ||
| European Regional Development Fund | ERDF | |
| Xunta de Galicia | ||
| Universidade da Coruña |
-
1
CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Universidade da Coruña/CISUG, for funding the open access charge.
-
2
CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).
-
3
Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC); Secretaría Xeral de Universidades; European Regional Development Fund; Xunta de Galicia Funding information
- ISSN: 02664720
- CODEN: EXSYE
- Source Type: Journal
- Original language: English
- DOI: 10.1111/exsy.12990
- Document Type: Article
- Publisher: John Wiley and Sons Inc
Jove, E.; CTC, Department of Industrial Engineering, CITIC, University of A Coruña, A Coruña, Spain;
© Copyright 2022 Elsevier B.V., All rights reserved.
Cited by 6 documents
(2025) Expert Systems with Applications
(2025) Technologies
(2024) Lecture Notes in Networks and Systems