• DocumentCode
    3374317
  • Title

    Signal trend identification with fuzzy methods

  • Author

    Wang, Xin ; Wei, Thomas Y C ; Reifman, Jaques ; Tsoukalas, Lefteri H.

  • Author_Institution
    Sch. of Nucl. Eng., Purdue Univ., West Lafayette, IN, USA
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    332
  • Lastpage
    335
  • Abstract
    A fuzzy logic-based methodology for online signal trend identification is introduced. Although signal trend identification is complicated by the presence of noise, fuzzy logic can help capture important features of online signals and classify incoming power plant signals into increasing, decreasing and steady-state trend categories. In order to verify the methodology, a code named PROTREN is developed and tested using plant data. The results indicate that the code is capable of detecting transients accurately, identifying trends reliably, and not misinterpreting a steady-state signal as a transient one
  • Keywords
    fuzzy logic; noise; power plants; signal classification; PROTREN; fuzzy logic; noise; online signal trend identification; power plant signal classification; steady-state signal; transient detection; transient signal; Data mining; Fuzzy logic; Inductors; Laboratories; Power generation; Power system reliability; Signal processing; Steady-state; Testing; Thermal management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on
  • Conference_Location
    Chicago, IL
  • ISSN
    1082-3409
  • Print_ISBN
    0-7695-0456-6
  • Type

    conf

  • DOI
    10.1109/TAI.1999.809813
  • Filename
    809813