A Machine Learning Approach to Generate Rules for Process Fault Diagnosis

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抄録

Expert systems can play a very important role in manufacturing processes by locating problems as soon as they arise. The most important ingredient in any expert system is knowledge. The current knowledge acquisition method is slow and tedious and there exist substantial difficulties in acquiring the knowledge for complex processes. An approach is proposed that makes use of the machine learning technique, C4.5, to generate a decision tree. The decision tree is translated into rules that are implemented into the expert system shell, G2. The rules are tested using a sensitivity analysis of the system. The approach works well, but depends on both the quality and quantity of available training data.

収録刊行物

  • Journal of chemical engineering of Japan

    Journal of chemical engineering of Japan 37(6), 691-697, 2004-06-01

    The Society of Chemical Engineers, Japan

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各種コード

  • NII論文ID(NAID)
    10013340627
  • NII書誌ID(NCID)
    AA00709658
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    00219592
  • NDL 記事登録ID
    6981492
  • NDL 雑誌分類
    ZP1(科学技術--化学・化学工業)
  • NDL 請求記号
    Z53-R395
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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