• DocumentCode
    497423
  • Title

    Risk Assessment Model of Automobile Defect Based on Gray Theory

  • Author

    Wang, Yan ; Wang, Yun-Song ; Zhang, Jin-Huan

  • Author_Institution
    Sch. of Civil & Environ. Eng., Univ. of Sci. & Technol. Beijing, BeiJing, China
  • Volume
    2
  • fYear
    2009
  • fDate
    11-12 April 2009
  • Firstpage
    317
  • Lastpage
    320
  • Abstract
    Risk assessment of automobile defect is classifying safety related risk level of manufacturing or designing defect. To carry out equivalent study between defect risk and failure defect, a risk flow route of automobile defect is developed introducing ETA method. A SPN function for risk assessment is setup based on Characteristics of automobile defect risk, and 3D matrix graph is introduced to describe overall risk. According to the scattered and fluctuant characteristics of automobile defect data, a risk forecast method based on gray theory is discussed. A risk assessment model of automobile defect is built; forecast of risk possibility is based on failure data. The model is corrected by residual discrimination. It is found that on gathering actual failure data from after-sales service, the gray model has a favorable applicability for forecast of risk possibility.
  • Keywords
    automobile manufacture; graph theory; grey systems; matrix algebra; risk management; safety; 3D matrix graph; ETA method; automobile defect; gray theory; risk assessment model; risk flow route; safety; Automobile manufacture; Automotive engineering; Government; Injuries; Predictive models; Risk analysis; Risk management; Road accidents; Time of arrival estimation; Vehicle safety; applicability; automobile; defect; model; risk assessment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
  • Conference_Location
    Zhangjiajie, Hunan
  • Print_ISBN
    978-0-7695-3583-8
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2009.621
  • Filename
    5203437