• DocumentCode
    901659
  • Title

    Predictive maintenance in intelligent-control-maintenance-management system for hydroelectric generating unit

  • Author

    Fu, Chuang ; Ye, Luqing ; Liu, Yongqian ; Yu, Ren ; Iung, Benoit ; Cheng, Yuanchu ; Zeng, Yuming

  • Author_Institution
    Coll. of Hydropower & Digital Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • Volume
    19
  • Issue
    1
  • fYear
    2004
  • fDate
    3/1/2004 12:00:00 AM
  • Firstpage
    179
  • Lastpage
    186
  • Abstract
    The predictive maintenance within the framework of intelligent-control-maintenance-management system (ICMMS) makes full use of all the information of control, maintenance, and technical management aspects to make right maintenance at the right time in the right place. In this paper, the three key elements of the predictive maintenance within the framework of ICMMS are presented. The ICMMS platform for hydroelectric generating unit, especially its maintenance function, is introduced. An artificial-neural-network (ANN)-based identification and diagnosis model is set up to implement the predictive maintenance of the electrohydraulic servomechanism in the hydroelectric generating unit. The tests show that the proposed strategy can guarantee ideal performance.
  • Keywords
    electrohydraulic control equipment; hydroelectric generators; intelligent control; maintenance engineering; neural nets; power generation control; servomechanisms; artificial neural network; diagnosis model; electrohydraulic servomechanism; failure model; hydroelectric generating unit; identification model; intelligent-control-maintenance-management system; predictive maintenance; Automation; Control systems; Electric breakdown; Electrohydraulics; Hydroelectric power generation; Intelligent systems; Job shop scheduling; Predictive maintenance; Predictive models; Servomechanisms;
  • fLanguage
    English
  • Journal_Title
    Energy Conversion, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8969
  • Type

    jour

  • DOI
    10.1109/TEC.2003.816600
  • Filename
    1268135