• DocumentCode
    2231027
  • Title

    Data Mining Applied to the Instrumentation Data Analysis of a Large Dam

  • Author

    Villwock, Rosangela ; Steiner, Maria Teresinha Arns ; Dyminski, Andréa Sell

  • Author_Institution
    UFPR, Curitiba
  • fYear
    2007
  • fDate
    20-24 Oct. 2007
  • Firstpage
    949
  • Lastpage
    954
  • Abstract
    Itaipu dam is one of the biggest dams in the world. There are more than 2200 instruments installed, periodically monitored since 1986. Due to the large amount of collected and stored data, as well as to the possibility of useful but hidden information, it is necessary to use techniques that may allow us to transform data into knowledge. Itaipu company just finished to apply the automatic acquisition system in approximately 210 instruments. With this work we intend to confirm the choice of the instruments that shall be automated focusing on one type of them, namely extensometers, located at the dam´s section F. This task was performed by applying mathematical models based on data mining techniques capable of analyzing and selecting the most relevant data. Among the techniques, we applied multivariate data analysis: principal components, factor and clustering analysis, in a variety of forms. The results were satisfactory as we show.
  • Keywords
    dams; data mining; geophysics computing; principal component analysis; Itaipu dam; automatic acquisition system; data mining; factor-clustering analysis; instrumentation data analysis; multivariate data analysis; principal components; Data analysis; Data mining; Displacement measurement; Instruments; Intelligent systems; Mathematical model; Monitoring; Safety; System analysis and design; Variable speed drives;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on
  • Conference_Location
    Rio de Janeiro
  • Print_ISBN
    978-0-7695-2976-9
  • Type

    conf

  • DOI
    10.1109/ISDA.2007.113
  • Filename
    4389730