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A distributed topology for identifying anomalies in an industrial environment
dc.contributor.author | Zayas-Gato, Francisco | |
dc.contributor.author | Michelena, Álvaro | |
dc.contributor.author | Jove, Esteban | |
dc.contributor.author | Casteleiro-Roca, José-Luis | |
dc.contributor.author | Quintián, Héctor | |
dc.contributor.author | Novais, Paulo | |
dc.contributor.author | Méndez Pérez, Juan Albino | |
dc.contributor.author | Calvo-Rolle, José Luis | |
dc.date.accessioned | 2024-07-01T11:45:52Z | |
dc.date.available | 2024-07-01T11:45:52Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Zayas-Gato, F., Michelena, Á., Jove, E. et al. A distributed topology for identifying anomalies in an industrial environment. Neural Comput & Applic 34, 20463–20476 (2022). https://doi.org/10.1007/s00521-022-07106-7 | es_ES |
dc.identifier.issn | 0941-0643 | |
dc.identifier.uri | http://hdl.handle.net/2183/37585 | |
dc.description | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. | es_ES |
dc.description.abstract | [Abstract] The devastating consequences of climate change have resulted in the promotion of clean energies, being the wind energy the one with greater potential. This technology has been developed in recent years following different strategic plans, playing special attention to wind generation. In this sense, the use of bicomponent materials in wind generator blades and housings is a widely spread procedure. However, the great complexity of the process followed to obtain this kind of materials hinders the problem of detecting anomalous situations in the plant, due to sensors or actuators malfunctions. This has a direct impact on the features of the final product, with the corresponding influence in the durability and wind generator performance. In this context, the present work proposes the use of a distributed anomaly detection system to identify the source of the wrong operation. With this aim, five different one-class techniques are considered to detect deviations in three plant components located in a bicomponent mixing machine installation: the flow meter, the pressure sensor and the pump speed. | es_ES |
dc.description.sponsorship | 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). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation.uri | https://doi.org/10.1007/s00521-022-07106-7 | es_ES |
dc.rights | Creative Commons Attribution 4.0 International License 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 | One-class | es_ES |
dc.subject | Control system | es_ES |
dc.subject | kNN | es_ES |
dc.subject | MST | es_ES |
dc.subject | NCBoP | es_ES |
dc.subject | PCA | es_ES |
dc.subject | SVDD | es_ES |
dc.title | A distributed topology for identifying anomalies in an industrial environment | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Neural Computing and Applications | es_ES |
UDC.volume | 34 | es_ES |
UDC.startPage | 20463 | es_ES |
UDC.endPage | 20476 | es_ES |
dc.identifier.doi | https://doi.org/10.1007/s00521-022-07106-7 |
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