• DocumentCode
    1993327
  • Title

    The Intelligence System of Failures Diagnosis for Robot Base on the Knowledge Decision-Making

  • Author

    Lin, Li ; Tie, Zhang ; Cunxi, Xie

  • Author_Institution
    Sch. of Mech. & Automotive Eng., South China Univ. of Technol., Guangzhou
  • Volume
    2
  • fYear
    2008
  • fDate
    21-22 Dec. 2008
  • Firstpage
    820
  • Lastpage
    822
  • Abstract
    Based on knowledge decision method, an intelligent diagnosis system for robot is introduced. The system combines intelligence decision technology and knowledge of robot failures diagnosis. One dimension structure of data-base is proposed for the knowledge-base. Each failure symptom has its exclusive record number therefore, the complicated nature language is changed to simple code description respective, and avoids to searching problem for the complicated nature language. The arithmetic of knowledge decision is proposed in the intelligence system for robot failures diagnosis. Considering the factors of failure event probability, and diagnosis knowledge validating weight value, methods are used which is compositor for each failure, and the DFS (deep first search) strategy to validate each failures. By this means, the efficiency of the intelligence system for failures diagnosis is improved.
  • Keywords
    decision making; failure analysis; fault diagnosis; intelligent robots; knowledge based systems; tree searching; code description; deep first search strategy; diagnosis knowledge validating weight value; failure event probability; intelligence system; intelligent diagnosis system; knowledge decision making; nature languages; robot base; robot failures diagnosis; Decision making; Educational robots; Educational technology; Intelligent robots; Intelligent systems; Intelligent vehicles; Robot sensing systems; Robotic assembly; Robotics and automation; Service robots; failures diagnosis; intelligence system; knowledge decision; robot;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Education Technology and Training, 2008. and 2008 International Workshop on Geoscience and Remote Sensing. ETT and GRS 2008. International Workshop on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3563-0
  • Type

    conf

  • DOI
    10.1109/ETTandGRS.2008.372
  • Filename
    5070486