• DocumentCode
    2630771
  • Title

    Condition monitoring and diagnosis of rotating machinery by Gram-Charlier expansion of vibration signal

  • Author

    Toyota, Toshio ; Niho, Tomoya ; Chen, Peng

  • Author_Institution
    Fac. of Comput. Sci. & Syst. Eng., Kyushu Inst. of Technol., Fukuoka, Japan
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    541
  • Abstract
    Here we present the new robust condition monitoring and diagnosis method based on the statistical hypothesis on vibration characteristics of the rotating machines in good condition. The hypothesis is that if the machine is in good condition, its probability density function of vibration signal follows the normal distribution in time domain. This method can lead to the robust failure diagnosis without any prior knowledge concerning vibration characteristics corresponding to specific failure to be detected
  • Keywords
    condition monitoring; fault diagnosis; knowledge based systems; mechanical engineering computing; statistical analysis; vibration measurement; condition monitoring; diagnosis; statistical hypothesis; vibration characteristics; vibration signal; Condition monitoring; Density functional theory; Feature extraction; Gaussian distribution; Intelligent structures; Intelligent systems; Knowledge engineering; Machine intelligence; Machinery; Rolling bearings;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
  • Conference_Location
    Brighton
  • Print_ISBN
    0-7803-6400-7
  • Type

    conf

  • DOI
    10.1109/KES.2000.884106
  • Filename
    884106