• DocumentCode
    519400
  • Title

    Engine Condition Monitoring Based on Grey AR Combination Model

  • Author

    Wang, Qiang ; Dai, Hui Sheng

  • Volume
    1
  • fYear
    2010
  • fDate
    6-7 March 2010
  • Firstpage
    215
  • Lastpage
    218
  • Abstract
    Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.
  • Keywords
    Abrasives; Condition monitoring; Engines; Graphics; Least squares approximation; Linear regression; Predictive models; Technology forecasting; Testing; Time series analysis; auto-regressive; condition monitoring; grey theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Challenges in Environmental Science and Computer Engineering (CESCE), 2010 International Conference on
  • Conference_Location
    Wuhan, China
  • Print_ISBN
    978-0-7695-3972-0
  • Electronic_ISBN
    978-1-4244-5924-7
  • Type

    conf

  • DOI
    10.1109/CESCE.2010.19
  • Filename
    5493095