Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional Autónoma de México
2020
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| _version_ | 1876484671355748352 |
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| author | Andrés M. Valencia |
| author_facet | Andrés M. Valencia |
| contents | Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants Andrés M. Valencia Jesús Caratar Gladys Caicedo Cristian Chamorro Ingeniería data mining training set failure diagnosis intelligent systems Knowledge discovery data 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. 2020 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47471676005 https://www.redalyc.org/journal/474/47471676005/ https://www.redalyc.org/journal/474/47471676005/html/ https://www.redalyc.org/journal/474/47471676005/47471676005.epub https://www.redalyc.org/journal/474/47471676005/movil 10.14482/INDES.30.1.303.661 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.6 Vol.18 |
| format | Artículo científico |
| id | redalyc_47471676005 |
| institution | Redalyc |
| language | en |
| publishDate | 2020 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants Andrés M. Valencia Ingeniería data mining training set failure diagnosis intelligent systems Knowledge discovery data Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants Andrés M. Valencia Jesús Caratar Gladys Caicedo Cristian Chamorro Ingeniería data mining training set failure diagnosis intelligent systems Knowledge discovery data 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. 2020 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47471676005 https://www.redalyc.org/journal/474/47471676005/ https://www.redalyc.org/journal/474/47471676005/html/ https://www.redalyc.org/journal/474/47471676005/47471676005.epub https://www.redalyc.org/journal/474/47471676005/movil 10.14482/INDES.30.1.303.661 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.6 Vol.18 |
| title | Proposal for a KDD-based procedure to obtain a set of intelligent systems training applied to the identification of failures in hydroelectric power plants |
| topic | Ingeniería data mining training set failure diagnosis intelligent systems Knowledge discovery data |
| url | https://www.redalyc.org/articulo.oa?id=47471676005 https://www.redalyc.org/journal/474/47471676005/ https://www.redalyc.org/journal/474/47471676005/html/ https://www.redalyc.org/journal/474/47471676005/47471676005.epub https://www.redalyc.org/journal/474/47471676005/movil |