• DocumentCode
    2269405
  • Title

    Classification and Recognition of Detecting Parameters for Cement Mill

  • Author

    Qian, Hui ; Wang, Xiaohong ; Yu, Hongliang

  • Author_Institution
    Sch. of Control Sci. & Eng., Univ. of Jinan, Jinan
  • Volume
    3
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    402
  • Lastpage
    406
  • Abstract
    In view of the complex milling process of cement raw material, aim at practical technological features of cement mill, all relative factors to process are measured and classified as faulty condition and normal condition. This article applies classification and recognition algorithm to detecting main parameters for cement mill, and then to judge the operating condition. In view of fault detect that bases on the signal to judge the trends of fault and give an alarm, the running patterns of system are built with an improved ART-2 cluster parsing algorithm under normal condition, carry on the correct recognition to the status of cement mill, in turn to take the right recognition of milling working condition.
  • Keywords
    ART neural nets; cement industry; fault diagnosis; milling; pattern clustering; pattern recognition; signal classification; signal detection; ART-2 cluster parsing algorithm; cement mill; faulty condition detection; milling process; signal classification algorithm; signal recognition algorithm; Classification algorithms; Clustering algorithms; Employee welfare; Fault detection; Fault diagnosis; Feeds; Milling machines; Pattern recognition; Signal analysis; Signal processing algorithms; ART-2; Mill condition recognition; cement mill; fault recognition; wavelet;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3497-8
  • Type

    conf

  • DOI
    10.1109/IITA.2008.329
  • Filename
    4740027