• DocumentCode
    3727553
  • Title

    Fault diagnosis for the ship electric propulsion system

  • Author

    Bing Li; Meiyuan Chen; Rongrong Wang; Lanyong Zhang

  • Author_Institution
    College of Automation, Harbin Engineering University, China
  • fYear
    2015
  • Firstpage
    714
  • Lastpage
    718
  • Abstract
    According to the actual condition of the ship electric propulsion system, the fault tree was built via analyzing the fault characteristics and reasons of the components of the system. The analytic hierarchy process was used to calculate the weight of the fault reasons and formed a corresponding intelligent fault tree. Some rules which were used to build the knowledge base of fault diagnosis expert system in Access could be extracted from the intelligent tree. The fault diagnosis system made use of the width-first search strategy and forward reasoning control strategy. The BP neural networks algorithm was used to make up the lack of a learning disability. The system could help the workers to diagnose and repair the faults of the marine electric propulsion system more quickly and reduce the cost.
  • Keywords
    "Propulsion","Fault trees","Fault diagnosis","Marine vehicles","Expert systems","Inverters"
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2015 11th International Conference on
  • Electronic_ISBN
    2157-9563
  • Type

    conf

  • DOI
    10.1109/ICNC.2015.7378078
  • Filename
    7378078