• DocumentCode
    2700056
  • Title

    Application of fuzzy rough sets in patterns recognition of bearing

  • Author

    Tian, Hao ; Kang, Xiao-Yong ; Zhang, Jun-Nuo ; Han, Shan-Shan

  • Author_Institution
    Dept. of Guns Eng., Ordnance Eng. Coll., Shijiazhuang, China
  • fYear
    2012
  • fDate
    15-18 June 2012
  • Firstpage
    731
  • Lastpage
    734
  • Abstract
    A method of patterns recognition was presented based on fuzzy rough sets. Dynamic clustering algorithm and method of analysis of variance is introduced to fuzzify the continuous condition attribute, and fuzzy membership functions is derived, which avoided losing information caused by discretization in rough set theory. F test is introduced to judge the valid analysis of clustering, which has overcome the disadvantage of determining artificially the class number of clustering. The fuzzy decision table obtained by attribute fuzzified is used to attributes reduction, then values of attributes are reducted, and clear and concise pattern rules are obtained. The application showed that the proposed algorithm can effective improve the pattern recognition accuracy.
  • Keywords
    decision tables; fuzzy set theory; machine bearings; pattern recognition; rough set theory; analysis of variance; attributes reduction; bearing; continuous condition attribute; dynamic clustering algorithm; fuzzy decision table; fuzzy membership functions; fuzzy rough sets; pattern recognition; rough set theory; Educational institutions; Fault diagnosis; Fuzzy sets; Heuristic algorithms; Pattern recognition; Rough sets; anaiysis of variance; attribute reduction; dynamic clustering; fuzzy-rough sets; patterns recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012 International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4673-0786-4
  • Type

    conf

  • DOI
    10.1109/ICQR2MSE.2012.6246333
  • Filename
    6246333