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Diagnóstico de fallas mediante una LSTM y una red elástica

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Diagnóstico de fallas mediante una LSTM y una red elástica

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dc.contributor.author Márquez-Vera, M. A. es_ES
dc.contributor.author López-Ortega, O. es_ES
dc.contributor.author Ramos-Velasco, L. E. es_ES
dc.contributor.author Ortega-Mendoza, R. M. es_ES
dc.contributor.author Fernández-Neri, B. J. es_ES
dc.contributor.author Zúñiga-Peña, N. S. es_ES
dc.date.accessioned 2021-04-15T10:26:55Z
dc.date.available 2021-04-15T10:26:55Z
dc.date.issued 2021-04-06
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/165204
dc.description.abstract [EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature. es_ES
dc.description.abstract [ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Fault diagnosis es_ES
dc.subject Wavelet transform es_ES
dc.subject Recurrent neural networks es_ES
dc.subject Independent component analysis es_ES
dc.subject Elastic net es_ES
dc.subject Diagnóstico de fallas es_ES
dc.subject Transformada Wavelet es_ES
dc.subject Redes neuronales recurrentes es_ES
dc.subject Análisis de componentes independientes es_ES
dc.subject Red elástica es_ES
dc.title Diagnóstico de fallas mediante una LSTM y una red elástica es_ES
dc.title.alternative Fault diagnosis in industrial process by using LSTM and an elastic net es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2020.13611
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Márquez-Vera, MA.; López-Ortega, O.; Ramos-Velasco, LE.; Ortega-Mendoza, RM.; Fernández-Neri, BJ.; Zúñiga-Peña, NS. (2021). Diagnóstico de fallas mediante una LSTM y una red elástica. Revista Iberoamericana de Automática e Informática industrial. 18(2):164-175. https://doi.org/10.4995/riai.2020.13611 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2020.13611 es_ES
dc.description.upvformatpinicio 164 es_ES
dc.description.upvformatpfin 175 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 18 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\13611 es_ES
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