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Building and using dynamic risk-informed diagnosis procedures for complex system accidents

Author

Listed:
  • Katrina M Groth
  • Matthew R Denman
  • Michael C Darling
  • Thomas B Jones
  • George F Luger

Abstract

Accidents pose unique challenges for operating crews in complex systems such as nuclear power plants, presenting limitations in plant status information and lack of detailed monitoring, diagnosis, and response planning support. Advances in severe accident simulation and dynamic probabilistic risk assessment provide an opportunity to garner detailed insight into accident scenarios. In this article, we demonstrate how to build and use a framework which leverages dynamic probabilistic risk assessment, simulation, and dynamic Bayesian networks to provide real-time monitoring and diagnostic support for severe accidents in a nuclear power plant. We use general purpose modeling technology, the dynamic Bayesian network, and adapt it for risk management of complex engineering systems. This article presents a prototype model for monitoring and diagnosing system states associated with loss of flow and transient overpower accidents in a generic sodium fast reactor. We discuss using this framework to create a risk-informed accident management framework called Safely Managing Accidental Reactor Transients procedures . This represents a new application of risk assessment, expanding probabilistic risk assessment techniques beyond static decision support into dynamic, real-time models which support accident diagnosis and management.

Suggested Citation

  • Katrina M Groth & Matthew R Denman & Michael C Darling & Thomas B Jones & George F Luger, 2020. "Building and using dynamic risk-informed diagnosis procedures for complex system accidents," Journal of Risk and Reliability, , vol. 234(1), pages 193-207, February.
  • Handle: RePEc:sae:risrel:v:234:y:2020:i:1:p:193-207
    DOI: 10.1177/1748006X18803836
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    Citations

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    Cited by:

    1. KIM, Junyung & ZHAO, Xingang & SHAH, Asad Ullah Amin & KANG, Hyun Gook, 2021. "System risk quantification and decision making support using functional modeling and dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Kaneko, Fujio & Yuzui, Tomohiro, 2023. "Novel method of dynamic event tree keeping the number of simulations in risk analysis small," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Moradi, Ramin & Groth, Katrina M., 2020. "Modernizing risk assessment: A systematic integration of PRA and PHM techniques," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Zhao, Yunfei & Smidts, Carol, 2021. "CMS-BN: A cognitive modeling and simulation environment for human performance assessment, part 1 — methodology," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Mei Liu & Boning Li & Hongjun Cui & Pin-Chao Liao & Yuecheng Huang, 2022. "Research Paradigm of Network Approaches in Construction Safety and Occupational Health," IJERPH, MDPI, vol. 19(19), pages 1-22, September.
    7. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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