IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v91y2006i2p191-199.html
   My bibliography  Save this article

A probabilistic approach for determining the control mode in CREAM

Author

Listed:
  • Kim, Man Cheol
  • Seong, Poong Hyun
  • Hollnagel, Erik

Abstract

The control mode is the core concept for the prediction of human performance in CREAM. In this paper, we propose a probabilistic method for determining the control mode which is a substitute for the existing deterministic method. The new method is based on a probabilistic model, a Bayesian network. This paper describes the mathematical procedure for developing the Bayesian network for determining the control mode. The Bayesian network developed in this paper is an extension of the existing deterministic method. Using the Bayesian network, we expect that we can get the best estimate of the control mode given the available data and information about the context. The mathematical background and procedure for developing equivalent Bayesian networks for given discrete functions provided in this paper can be applied to other discrete functions to develop probabilistic models.

Suggested Citation

  • Kim, Man Cheol & Seong, Poong Hyun & Hollnagel, Erik, 2006. "A probabilistic approach for determining the control mode in CREAM," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 191-199.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:2:p:191-199
    DOI: 10.1016/j.ress.2004.12.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832005000347
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2004.12.003?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.

    Citations

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


    Cited by:

    1. Bing Wu & Xinping Yan & Yang Wang & C. Guedes Soares, 2017. "An Evidential Reasoning‐Based CREAM to Human Reliability Analysis in Maritime Accident Process," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1936-1957, October.
    2. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud, 2020. "A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Montewka, Jakub & Goerlandt, Floris & Innes-Jones, Gemma & Owen, Douglas & Hifi, Yasmine & Puisa, Romanas, 2017. "Enhancing human performance in ship operations by modifying global design factors at the design stage," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 283-300.
    5. Marzio Marseguerra & Enrico Zio & Massimo Librizzi, 2007. "Human Reliability Analysis by Fuzzy “CREAM”," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 137-154, February.
    6. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud & van Gelder, Pieter, 2020. "BN-SLIM: A Bayesian Network methodology for human reliability assessment based on Success Likelihood Index Method (SLIM)," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    7. Mojgan Aalipour & Yonas Zewdu Ayele & Abbas Barabadi, 2016. "Human reliability assessment (HRA) in maintenance of production process: a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(2), pages 229-238, June.
    8. Patriarca, Riccardo & Ramos, Marilia & Paltrinieri, Nicola & Massaiu, Salvatore & Costantino, Francesco & Di Gravio, Giulio & Boring, Ronald Laurids, 2020. "Human reliability analysis: Exploring the intellectual structure of a research field," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    9. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    10. Reer, Bernhard, 2008. "Review of advances in human reliability analysis of errors of commission—Part 2: EOC quantification," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1105-1122.
    11. Sun, Zhiqiang & Li, Zhengyi & Gong, Erling & Xie, Hongwei, 2012. "Estimating Human Error Probability using a modified CREAM," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 28-32.
    12. Groth, Katrina M. & Swiler, Laura P., 2013. "Bridging the gap between HRA research and HRA practice: A Bayesian network version of SPAR-H," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 33-42.
    13. Medina-Oliva, G. & Weber, P. & Iung, B., 2013. "PRM-based patterns for knowledge formalisation of industrial systems to support maintenance strategies assessment," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 38-56.
    14. Di Pasquale, Valentina & Miranda, Salvatore & Iannone, Raffaele & Riemma, Stefano, 2015. "A Simulator for Human Error Probability Analysis (SHERPA)," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 17-32.
    15. Zheng, Xi & Bolton, Matthew L. & Daly, Christopher & Biltekoff, Elliot, 2020. "The development of a next-generation human reliability analysis: Systems analysis for formal pharmaceutical human reliability (SAFPHâ–ª)," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    16. Asadayoobi, N. & Taghipour, S. & Jaber, M.Y., 2022. "Predicting human reliability based on probabilistic mission completion time using Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    17. Groth, Katrina M. & Smith, Curtis L. & Swiler, Laura P., 2014. "A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 32-40.

    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:91:y:2006:i:2:p:191-199. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.