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Novel adaptive approach for anomaly detection in nonlinear and time-varying industrial systems
dc.contributor.author | Michelena, Álvaro | |
dc.contributor.author | Zayas-Gato, Francisco | |
dc.contributor.author | Jove, Esteban | |
dc.contributor.author | Casteleiro-Roca, José-Luis | |
dc.contributor.author | Quintián, Héctor | |
dc.contributor.author | Fontenla-Romero, Óscar | |
dc.contributor.author | Calvo-Rolle, José Luis | |
dc.date.accessioned | 2024-05-21T08:35:07Z | |
dc.date.available | 2024-05-21T08:35:07Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Álvaro Michelena, Francisco Zayas-Gato, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, Óscar Fontenla-Romero, José Luis Calvo-Rolle, Novel adaptive approach for anomaly detection in nonlinear and time-varying industrial systems, Logic Journal of the IGPL, 2024;, jzae070, https://doi.org/10.1093/jigpal/jzae070 | es_ES |
dc.identifier.issn | 1368-9894 | |
dc.identifier.uri | http://hdl.handle.net/2183/36558 | |
dc.description | Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.description.abstract | [Abstract] The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate the proposal, two different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2023/49 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Oxford University Press | es_ES |
dc.relation | info:eu-repo/grantAgreement/MUNI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/FPU21%2F00932/ES | es_ES |
dc.relation.uri | https://doi.org/10.1093/jigpal/jzae070 | es_ES |
dc.rights | Creative Commons Attribution License CC BY 4.0 http://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Anomaly detection | es_ES |
dc.subject | Fault detection | es_ES |
dc.subject | Online identification | es_ES |
dc.subject | Centroids | es_ES |
dc.title | Novel adaptive approach for anomaly detection in nonlinear and time-varying industrial systems | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Logic Journal of the IGPL | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 16 | es_ES |
dc.identifier.doi | https://doi.org/10.1093/jigpal/jzae070 |
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