• DocumentCode
    1792775
  • Title

    Trend-weighted rule-based expert system for process diagnosis

  • Author

    Curvelo de Souza, Danilo ; Doria Neto, Adriao Duarte ; Guedes, Luiz Affonso

  • Author_Institution
    Dept. of Comput. Eng., Fed. Univ. of Rio Grande do Norte, Natal, Brazil
  • fYear
    2014
  • fDate
    16-19 Sept. 2014
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This paper presents and innovative technique-referred to as trend-weighted rule-based expert system (TWRBES) - grounded in the integration of two existing tools of the artificial intelligence field, expert systems (ES) and qualitative trend analysis (QTA). The main goal of this approach is to benefit of the main advantages associated with each of the techniques used, such as the ability to represent knowledge through rules and the ability to extract the behavior and the trends of a continuous signal. Such integration allows a direct purpose in industrial environment applications, especially in the intelligent automation field. This paper introduces this technique and preliminary results obtained from applying it to industrial process diagnosis.
  • Keywords
    artificial intelligence; expert systems; failure analysis; fault diagnosis; knowledge representation; production engineering computing; QTA; TWRBES; artificial intelligence; industrial process diagnosis; knowledge representation; qualitative trend analysis; trend-weighted rule-based expert system; Accuracy; Expert systems; Market research; Monitoring; Polynomials; diagnosis; expert system; intelligent automation; qualitative trend analysis; rules;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Technology and Factory Automation (ETFA), 2014 IEEE
  • Conference_Location
    Barcelona
  • Type

    conf

  • DOI
    10.1109/ETFA.2014.7005325
  • Filename
    7005325