IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v226y2012i2p182-193.html
   My bibliography  Save this article

Downwards inference: Bayesian analysis of overlapping higher-level data sets of complex binary-state on-demand systems

Author

Listed:
  • C Jackson
  • A Mosleh

Abstract

A developed Bayesian approach for generating inference from multiple overlapping higher level data sets on component failure probabilities within complex on-demand systems is presented in this paper. Data sets are overlapping if they are drawn from the same process or system at the same time. Alternate methodologies are typically strictly limited to analyzing non-overlapping data, which can both lose or incorrectly infer information if the data is overlapping in nature. The methodology outlined in this paper is based on typical understanding of system logic represented using fault-trees, reliability block diagrams or another equivalent representation. Structure functions of the system at relevant sensor locations (developed from the system logic) in terms of component states are used in conjunction with the probability of all possible system states (or all possible state vectors) to generate the likelihood function of overlapping evidence. This forms the basis of the likelihood function used in the Bayesian analysis of the overlapping data sets.

Suggested Citation

  • C Jackson & A Mosleh, 2012. "Downwards inference: Bayesian analysis of overlapping higher-level data sets of complex binary-state on-demand systems," Journal of Risk and Reliability, , vol. 226(2), pages 182-193, April.
    • Handle: RePEc:sae:risrel:v:226:y:2012:i:2:p:182-193
    DOI: 10.1177/1748006X11402423
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X11402423
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X11402423?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Graves, T.L. & Hamada, M.S. & Klamann, R. & Koehler, A. & Martz, H.F., 2007. "A fully Bayesian approach for combining multi-level information in multi-state fault tree quantification," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1476-1483.
    2. David V. Mastran, 1976. "Incorporating Component and System Test Data into the Same Assessment: A Bayesian Approach," Operations Research, INFORMS, vol. 24(3), pages 491-499, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jackson, Chris & Mosleh, Ali, 2016. "Bayesian inference with overlapping data: Reliability estimation of multi-state on-demand continuous life metric systems with uncertain evidence," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 124-135.
    2. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jackson, Chris & Mosleh, Ali, 2016. "Bayesian inference with overlapping data: Reliability estimation of multi-state on-demand continuous life metric systems with uncertain evidence," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 124-135.
    2. C Jacksonn & A Mosleh, 2012. "Bayesian inference with overlapping data: methodology for reliability estimation of multi-state on-demand systems," Journal of Risk and Reliability, , vol. 226(3), pages 283-294, June.
    3. Jackson, Chris & Mosleh, Ali, 2012. "Bayesian inference with overlapping data for systems with continuous life metrics," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 217-231.
    4. Yan-Feng Li & Jinhua Mi & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers," Journal of Risk and Reliability, , vol. 229(6), pages 530-541, December.
    5. Babaleye, Ahmed O. & Kurt, Rafet Emek & Khan, Faisal, 2019. "Safety analysis of plugging and abandonment of oil and gas wells in uncertain conditions with limited data," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 133-141.
    6. Chenxi Liu & Nan Chen & Jianing Yang, 2015. "New method for multi-state system reliability analysis based on linear algebraic representation," Journal of Risk and Reliability, , vol. 229(5), pages 469-482, October.
    7. Yontay, Petek & Pan, Rong, 2016. "A computational Bayesian approach to dependency assessment in system reliability," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 104-114.
    8. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    9. Zheng, Yi-Xuan & Xiahou, Tangfan & Liu, Yu & Xie, Chaoyang, 2021. "Structure function learning of hierarchical multi-state systems with incomplete observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Cui, L.X. & Du, Yi-Mu & Sun, C.P., 2023. "On system reliability for time-varying structure," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Ebrahimi, Nader, 2009. "Bayesian framework for prediction of future number of failures from a single group of units in the field," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 773-775.
    12. Roger Zoh & Alyson Wilson & Scott Vander Wiel & Earl Lawrence, 2018. "The negative log-gamma prior distribution for Bayesian assessment of system reliability," Journal of Risk and Reliability, , vol. 232(3), pages 308-319, June.
    13. Anderson-Cook, Christine M. & Crowder, Stephen & Huzurbazar, Aparna V. & Lorio, John & Ringland, James & Wilson, Alyson G., 2011. "Quantifying reliability uncertainty from catastrophic and margin defects: A proof of concept," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1063-1075.
    14. Jørgen Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03634370, HAL.
    15. Jørgen Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Post-Print hal-03634370, HAL.
    16. Wang, Lizhi & Pan, Rong & Li, Xiaoyang & Jiang, Tongmin, 2013. "A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 38-47.
    17. Yang, Lechang & Wang, Pidong & Wang, Qiang & Bi, Sifeng & Peng, Rui & Behrensdorf, Jasper & Beer, Michael, 2021. "Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    18. Yılmaz, Emre & German, Brian J. & Pritchett, Amy R., 2023. "Optimizing resource allocations to improve system reliability via the propagation of statistical moments through fault trees," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    19. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    20. Jorgen-Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Annals of Operations Research, Springer, vol. 326(1), pages 295-316, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:226:y:2012:i:2:p:182-193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.