IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v170y2018icp146-158.html
   My bibliography  Save this article

System reliability assessment with multilevel information using the Bayesian melding method

Author

Listed:
  • Guo, Jian
  • (Steven) Li, Zhaojun
  • (Judy) Jin, Jionghua

Abstract

This paper investigates the Bayesian melding method (BMM) for system reliability analysis by effectively integrating various available sources of expert knowledge and data at both subsystem and system levels. The integration of multiple priors is investigated under both linear and geometric pooling methods. The aggregated system prior distributions using various pooling methods including the BMM are evaluated and compared. Based on these integrated and updated prior distributions and three scenarios of data availability from a system and/or subsystems, methods for posterior system reliability inference are proposed. Computational challenges for posterior inferences using the sophisticated BMM are addressed using the adaptive sampling importance re-sampling (SIR) method. A numerical example with simulation results illustrates the applications of the proposed methods and provides insights for system reliability analysis using multilevel information.

Suggested Citation

  • Guo, Jian & (Steven) Li, Zhaojun & (Judy) Jin, Jionghua, 2018. "System reliability assessment with multilevel information using the Bayesian melding method," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 146-158.
    • Handle: RePEc:eee:reensy:v:170:y:2018:i:c:p:146-158
    DOI: 10.1016/j.ress.2017.09.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183201630388X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2017.09.020?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wilson, Alyson G. & McNamara, Laura A. & Wilson, Gregory D., 2007. "Information integration for complex systems," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 121-130.
    2. 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.
    3. Cooke, Roger M. & Goossens, Louis L.H.J., 2008. "TU Delft expert judgment data base," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 657-674.
    4. Robert L. Winkler, 1968. "The Consensus of Subjective Probability Distributions," Management Science, INFORMS, vol. 15(2), pages 61-75, October.
    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. Xu, Yingchun & Yao, Wen & Zheng, Xiaohu & Chen, Xiaoqian, 2020. "An iterative information integration method for multi-level system reliability analysis based on Bayesian Melding Method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Jia, Xiang & Guo, Bo, 2022. "Reliability analysis for complex system with multi-source data integration and multi-level data transmission," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    5. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    6. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Huimin Wang & Zhaojun Steven Li, 2022. "An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems," Journal of Risk and Reliability, , vol. 236(3), pages 495-507, June.
    8. Wang, Bo & Jiang, Ping & Guo, Bo, 2024. "A Bayesian design method for monopropellant engine system reliability qualification test plan," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    9. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Chen, Xianqi, 2019. "Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 123-142.

    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. Hanea, D.M. & Jagtman, H.M. & van Alphen, L.L.M.M. & Ale, B.J.M., 2010. "Quantitative and qualitative analysis of the expert and non-expert opinion in fire risk in buildings," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 729-741.
    2. Vimal Vijayan & Sanjay K Chaturvedi & Ritesh Chandra, 2020. "A failure interaction model for multicomponent repairable systems," Journal of Risk and Reliability, , vol. 234(3), pages 470-486, June.
    3. Eggstaff, Justin W. & Mazzuchi, Thomas A. & Sarkani, Shahram, 2014. "The effect of the number of seed variables on the performance of Cooke′s classical model," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 72-82.
    4. Makariou, Despoina & Barrieu, Pauline & Tzougas, George, 2021. "A finite mixture modelling perspective for combining experts’ opinions with an application to quantile-based risk measures," LSE Research Online Documents on Economics 110763, London School of Economics and Political Science, LSE Library.
    5. Despoina Makariou & Pauline Barrieu & George Tzougas, 2021. "A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures," Risks, MDPI, vol. 9(6), pages 1-25, June.
    6. Justin W. Eggstaff & Thomas A. Mazzuchi & Shahram Sarkani, 2014. "The Development of Progress Plans Using a Performance‐Based Expert Judgment Model to Assess Technical Performance and Risk," Systems Engineering, John Wiley & Sons, vol. 17(4), pages 375-391, December.
    7. Geovanna Hinojoza-Castro & Montserrat Gómez-Delgado & Wenseslao Plata-Rocha, 2022. "Real Estate Developers as Agents in the Simulation of Urban Sprawl," Sustainability, MDPI, vol. 14(15), pages 1-12, July.
    8. Wilson, Alyson G. & Huzurbazar, Aparna V., 2007. "Bayesian networks for multilevel system reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1413-1420.
    9. Fei Xiong & Yun Liu & Zhenjiang Zhang, 2011. "Dynamics With Co-Evolution Of Individual Inclination And Opinion," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 51-62.
    10. Joseph Kadane & Javier Girón & Daniel Peña & Peter Fishburn & Simon French & D. Lindley & Giovanni Parmigiani & Robert Winkler, 1993. "Several Bayesians: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 2(1), pages 1-32, December.
    11. Karner, Alex & Niemeier, Deb, 2013. "Civil rights guidance and equity analysis methods for regional transportation plans: a critical review of literature and practice," Journal of Transport Geography, Elsevier, vol. 33(C), pages 126-134.
    12. Hanea, A.M. & McBride, M.F. & Burgman, M.A. & Wintle, B.C. & Fidler, F. & Flander, L. & Twardy, C.R. & Manning, B. & Mascaro, S., 2017. "I nvestigate D iscuss E stimate A ggregate for structured expert judgement," International Journal of Forecasting, Elsevier, vol. 33(1), pages 267-279.
    13. Laura Diaz Anadon & Erin Baker & Valentina Bosetti & Lara Aleluia Reis, 2016. "Expert views - and disagreements - about the potential of energy technology R&D," Climatic Change, Springer, vol. 136(3), pages 677-691, June.
    14. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    15. Colson, Abigail R. & Cooke, Roger M., 2017. "Cross validation for the classical model of structured expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 109-120.
    16. Mohammad J. Abdolmohammadi & Paul D. Berger, 1986. "A test of the accuracy of probability assessment techniques in auditing," Contemporary Accounting Research, John Wiley & Sons, vol. 3(1), pages 149-165, September.
    17. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    18. Mohammad J. Abdolmohammadi & Paul D. Berger, 1986. "Une expérience sur la précision des techniques d‘évaluation des probabilités en vérification," Contemporary Accounting Research, John Wiley & Sons, vol. 3(1), pages 166-183, September.
    19. Erin Baker & Olaitan Olaleye, 2013. "Combining Experts: Decomposition and Aggregation Order," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1116-1127, June.
    20. Anca M. Hanea & Marissa F. McBride & Mark A. Burgman & Bonnie C. Wintle, 2018. "The Value of Performance Weights and Discussion in Aggregated Expert Judgments," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1781-1794, September.

    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:eee:reensy:v:170:y:2018:i:c:p:146-158. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.