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Extraction Operation Know-How from Historical Operation Data

- Using Characterization Method of Time Series Data and Data Mining Method -

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3214))

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Abstract

In these days, it is very difficult to hand down experts’ operation know-how to beginner, because of operation technique of a large and highly complex plant and reducing operators. On the other hand, data mining methods (See5, naive bayes, k-nearest neighbor, and so on) has been proposed as knowledge discovering methods from a huge amount of data. See5 outputs decision trees or IF-THEN rules as data mining results. However, See5 cannot recognize data as time series. In this study, an extraction method of experts’ operation know-how from historical operation data is proposed. Furthermore efficiencies of the proposed method are demonstrated by numerical experiments using a dynamic simulator.

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Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Kyushu University, Fukuoka, 812-8581, Japan

    Kazuhiro Takeda & Yoshifumu Tsuge

  2. Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, 808-0135, Japan

    Hisayoshi Matsuyama

Authors
  1. Kazuhiro Takeda
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  2. Yoshifumu Tsuge
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  3. Hisayoshi Matsuyama
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Takeda, K., Tsuge, Y., Matsuyama, H. (2004). Extraction Operation Know-How from Historical Operation Data. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_48

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  • DOI: https://doi.org/10.1007/978-3-540-30133-2_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23206-3

  • Online ISBN: 978-3-540-30133-2

  • eBook Packages: Springer Book Archive

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