• DocumentCode
    3395072
  • Title

    Reliability analysis of phased-mission systems using Bayesian networks

  • Author

    Liu, Dong ; Zhang, Chunyuan ; Xing, Weiyan ; Li, Rui

  • Author_Institution
    Nat. Univ. of Defense Technol., Changsha
  • fYear
    2008
  • fDate
    28-31 Jan. 2008
  • Firstpage
    21
  • Lastpage
    26
  • Abstract
    The paper presents a Bayesian networks (BN) based method to analyze the reliability of phased-mission systems (PMS). The method includes three steps. Firstly, each phase of PMS is represented by a BN framework, named phase-BN. Then, in order to express the dependences across phases, all the phase-BN are combined by (1) connecting the root nodes that represent the same component but belong to different phases, and (2) connecting the leaf nodes of the phase-BN with a new node that represents the whole PMS mission. The new constructed BN is named PMS-BN. Lastly, the reliability analysis is performed by a discrete-time BN modeling acting on PMS-BN. Two examples are used to expatiate on the proposed approach. The PMS-BN based method provides a new way to analyze PMS, especially those with dynamic phases. Further, based on PMS-BN, fault diagnoses and sensitivity analysis can be performed easily.
  • Keywords
    belief networks; fault diagnosis; reliability; safety; sensitivity analysis; bayesian networks; fault diagnoses; phased-mission systems; reliability analysis; sensitivity analysis; Bayesian methods; Binary decision diagrams; Boolean functions; Data structures; Failure analysis; Fault trees; Joining processes; Performance analysis; Sensitivity analysis; System testing; Bayesian networks; phased-mission systems; reliability analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Reliability and Maintainability Symposium, 2008. RAMS 2008. Annual
  • Conference_Location
    Las Vegas, NV
  • ISSN
    0149-144X
  • Print_ISBN
    978-1-4244-1460-4
  • Electronic_ISBN
    0149-144X
  • Type

    conf

  • DOI
    10.1109/RAMS.2008.4925763
  • Filename
    4925763