dor_id: 4110271

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590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1364/796

100.1.#.a: Valencia, Andrés M.; Caratar, Jesús; Caicedo, Gladys; Chamorro, Cristian

524.#.#.a: Valencia, et al. (2020). Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants. Journal of Applied Research and Technology; Vol. 18 Núm. 6, 2020; 376-389. Recuperado de https://repositorio.unam.mx/contenidos/4110271

245.1.0.a: Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2020

264.#.1.c: 2020-12-31

653.#.#.a: knowledge discovery data; data mining; intelligent systems; failure diagnosis; training set; hydroelectric power plant

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/1364

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041.#.7.h: eng

520.3.#.a: This paper presents a procedure based on KDD (Knowledge Discovery Data), which allows the analysis of a data set to obtain structured information from the behavior of the system under specific conditions, such as system failure conditions at a hydroelectric power plant. By applying this procedure, the information obtained, it is structured in such a mode so that it can be used on the training of intelligent systems focused on fault diagnosis. The former procedure is necessary in the intelligent systems development stage because obtaining an effective training set requires extreme time and effort. The procedure was applied in the historical records of the Amaime hydroelectric power plant, located in Palmira, Valle del Cauca, Colombia, aiming to obtain patterns of behavior of the protection system which can be translated to different failures. This was possible by integrating a data mining technique such as hierarchical clustering and the statistical technique called the interpolation function. The main achievement of this work is to present a structured procedure that reduces the time to obtain a training set. In this specific case, the training set for mechanical failure of a hydroelectric power station was obtained, which can be used in the development of an intelligent system for failures diagnosis.

773.1.#.t: Journal of Applied Research and Technology; Vol. 18 Núm. 6 (2020); 376-389

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 376-389

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2020.18.6.1364

harvesting_date: 2023-11-08 13:10:00.0

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file_creator: Yolanda G.G.

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Artículo

Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants

Valencia, Andrés M.; Caratar, Jesús; Caicedo, Gladys; Chamorro, Cristian

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Valencia, et al. (2020). Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants. Journal of Applied Research and Technology; Vol. 18 Núm. 6, 2020; 376-389. Recuperado de https://repositorio.unam.mx/contenidos/4110271

Descripción del recurso

Autor(es)
Valencia, Andrés M.; Caratar, Jesús; Caicedo, Gladys; Chamorro, Cristian
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants
Fecha
2020-12-31
Resumen
This paper presents a procedure based on KDD (Knowledge Discovery Data), which allows the analysis of a data set to obtain structured information from the behavior of the system under specific conditions, such as system failure conditions at a hydroelectric power plant. By applying this procedure, the information obtained, it is structured in such a mode so that it can be used on the training of intelligent systems focused on fault diagnosis. The former procedure is necessary in the intelligent systems development stage because obtaining an effective training set requires extreme time and effort. The procedure was applied in the historical records of the Amaime hydroelectric power plant, located in Palmira, Valle del Cauca, Colombia, aiming to obtain patterns of behavior of the protection system which can be translated to different failures. This was possible by integrating a data mining technique such as hierarchical clustering and the statistical technique called the interpolation function. The main achievement of this work is to present a structured procedure that reduces the time to obtain a training set. In this specific case, the training set for mechanical failure of a hydroelectric power station was obtained, which can be used in the development of an intelligent system for failures diagnosis.
Tema
knowledge discovery data; data mining; intelligent systems; failure diagnosis; training set; hydroelectric power plant
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces