• DocumentCode
    2935812
  • Title

    An Ontology Modeling Method of Mechanical Fault Diagnosis System Based on RSM

  • Author

    Wen, Hong ; Zhe, YinLuan ; Zhang, Huifu ; Chen, Anhua ; Liu, Deshun

  • Author_Institution
    Hunan Univ. of Sci. & Technol., Xiangtan, China
  • fYear
    2009
  • fDate
    12-14 Oct. 2009
  • Firstpage
    408
  • Lastpage
    409
  • Abstract
    The intelligent level and diagnostic accuracy of mechanical fault diagnosis system depend on the knowledge quantity and quality in its library. While fusing existing knowledge is an important method to increase the knowledge quantity and quality in library. Accordingly, this paper using resource space model (RSM) of knowledge grid (KG) to classify and manage the fault diagnosis knowledge, then proposed an ontology modeling method of mechanical fault diagnosis system. Based on the method, we using protege 4 to construct an ontology of AC motor faults diagnosis.
  • Keywords
    AC motors; fault diagnosis; knowledge management; ontologies (artificial intelligence); AC motor faults diagnosis; RSM; knowledge grid; knowledge quality; knowledge quantity; library; mechanical fault diagnosis system; ontology modeling method; protege 4; resource space model; Fault diagnosis; Ontologies; mechanical fault diagnosis; ontology; protege; resource space model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on
  • Conference_Location
    Zhuhai
  • Print_ISBN
    978-0-7695-3810-5
  • Type

    conf

  • DOI
    10.1109/SKG.2009.57
  • Filename
    5370501