• DocumentCode
    2180479
  • Title

    Estimating component reliabilities from incomplete system failure data

  • Author

    Huairui Guo ; Szidarovszky, F. ; Pengying Niu

  • Author_Institution
    ReliaSoft Corp., Tucson, AZ, USA
  • fYear
    2013
  • fDate
    28-31 Jan. 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Engineers often need to estimate component reliabilities from system failure data that are obtained either from in-house tests or field operations. The components that caused the system failures usually can be identified by checking the failed systems; however, in some cases, the exact cause of failure is very difficult or impossible to identify. For the latter case, the information on the system failures is not complete, and such data are usually called masked failure data. In this paper, we propose a method for estimating component reliabilities from masked system failure data. Solutions for systems with serial and parallel configurations are provided. The failure distribution of each component is obtained from the proposed method, and is then used to calculate the probability that a system failure is caused by the given component when the exact time or an interval time of the system failure is known. This probability is very useful since it can be used to decide which component should be analyzed first when a system failure occurs, depending on the failure time.
  • Keywords
    failure analysis; probability; reliability; component reliability estimation; failure distribution; incomplete system failure data; probability; Equations; Mathematical model; Maximum likelihood estimation; Probability; Reliability engineering; Suspensions; masked failure data; maximum likelihood estimate; parallel systems; series systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Reliability and Maintainability Symposium (RAMS), 2013 Proceedings - Annual
  • Conference_Location
    Orlando, FL
  • ISSN
    0149-144X
  • Print_ISBN
    978-1-4673-4709-9
  • Type

    conf

  • DOI
    10.1109/RAMS.2013.6517765
  • Filename
    6517765