• DocumentCode
    3520263
  • Title

    Research of intelligent condition monitoring model for AUV

  • Author

    Yujia Wang ; Mingjun Zhang ; Ruichen Sun

  • Author_Institution
    College of Mechanical and Electrical Engineering, Harbin Engineering University
  • Volume
    2
  • fYear
    2004
  • fDate
    15-19 June 2004
  • Firstpage
    1707
  • Lastpage
    1711
  • Abstract
    An intelligent condition monitoring model for propellers and rudder of autonomous underwater vehicles (AUVs) was proposed, which was based on the FALCON with a 3-step learning algorithm. The steps of the algorithm included initialization with fuzzy C clustering, rules extraction with maximum weights matrix and parameters fine-tuning with GA. It constructed the configuration of the model, analyzed the process of the monitoring, and discussed the method of evaluation for the model. The results of the computer simulation by actual experiment data of a certain AUV shows that the condition monitoring model proposed in this article is feasible and prove that the learning algorithm for the FALCON is effective.
  • Keywords
    Algorithm design and analysis; Clustering algorithms; Computerized monitoring; Condition monitoring; Control system synthesis; Data mining; Fuzzy logic; Neural networks; Propellers; Underwater vehicles; FALCON; autonomous underwater vehicle; condition monitoring; learning algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
  • Conference_Location
    Hangzhou, China
  • Print_ISBN
    0-7803-8273-0
  • Type

    conf

  • DOI
    10.1109/WCICA.2004.1340963
  • Filename
    1340963