• DocumentCode
    3508852
  • Title

    An on-line monitoring and diagnostic method of rolling element bearing with AI

  • Author

    Shao, Yimin ; Nezu, Kiktto

  • Author_Institution
    Dept. of Mech. Eng., Gunma Univ., Japan
  • fYear
    1995
  • fDate
    26-28 Jul 1995
  • Firstpage
    1543
  • Lastpage
    1548
  • Abstract
    A new concept of the degree of creditability of parameter value variations (DCPV factor) is proposed in this paper to solve problem that on-line monitoring and failure diagnosis of rolling element bearings are affected by monitoring parameter value variations caused by the intrusive vibration signals. Using the factor of the degree of creditability and the basic principle of expert systems, an on-line monitoring and diagnostic method of rolling element bearings with AI is developed. The technique enhances traditional vibration analysis and provides a means of automating the monitoring and diagnosis of a vibrating device
  • Keywords
    computerised monitoring; diagnostic expert systems; fuzzy logic; mechanical engineering; monitoring; degree of creditability; diagnostic method; expert systems; intrusive vibration signals; online monitoring; rolling element bearing; vibrating device; Accelerometers; Artificial intelligence; Computerized monitoring; Condition monitoring; Diagnostic expert systems; Pulleys; Rolling bearings; Surface cracks; Testing; Vibrations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE '95. Proceedings of the 34th SICE Annual Conference. International Session Papers
  • Conference_Location
    Hokkaido
  • Print_ISBN
    0-7803-2781-0
  • Type

    conf

  • DOI
    10.1109/SICE.1995.526964
  • Filename
    526964