Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

Bao, Han; Zhang, Hongbin; Shorthill, Tate; Chen, Edward; Lawrence, Svetlana

doi:10.1016/j.ress.2022.108973

Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

Journal Article · Sun Nov 13 00:00:00 EST 2022 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2022.108973· OSTI ID:1974870

^[1]; Zhang, Hongbin ^[2]; Shorthill, Tate ^[3]; Chen, Edward ^[4]; Lawrence, Svetlana ^[1]

Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Terrapower, Bellevue, WA (United States)
University of Pittsburgh (United States)
North Carolina State University, Raleigh, NC (United States)

Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. Here this paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented here in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1974870

Alternate ID(s):: OSTI ID: 1899216

Report Number(s):: INL/JOU-22-66262-Revision-0

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Journal Issue: - Vol. 230; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (16)

Common cause failure analysis: A critique and some suggestions Parry, Gareth W. Reliability Engineering & System Safety, Vol. 34, Issue 3 https://doi.org/10.1016/0951-8320(91)90106-H	journal	January 1991
Qualitative cause-defense matrices: Engineering tools to support the analysis and prevention of common cause failures Paula, Henrique M.; Campbell, David J.; Rasmuson, Dale M. Reliability Engineering & System Safety, Vol. 34, Issue 3 https://doi.org/10.1016/0951-8320(91)90110-S	journal	January 1991
Hazard analysis for identifying common cause failures of digital safety systems using a redundancy-guided systems-theoretic approach Bao, Han; Shorthill, Tate; Zhang, Hongbin Annals of Nuclear Energy, Vol. 148 https://doi.org/10.1016/j.anucene.2020.107686	journal	December 2020
A novel approach for software reliability analysis of digital instrumentation and control systems in nuclear power plants Shorthill, Tate; Bao, Han; Zhang, Hongbin Annals of Nuclear Energy, Vol. 158 https://doi.org/10.1016/j.anucene.2021.108260	journal	August 2021
Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review Lin, Linyu; Bao, Han; Dinh, Nam Annals of Nuclear Energy, Vol. 160 https://doi.org/10.1016/j.anucene.2021.108362	journal	September 2021
A new method for explicit modelling of single failure event within different common cause failure groups Kančev, Duško; Čepin, Marko Reliability Engineering & System Safety, Vol. 103 https://doi.org/10.1016/j.ress.2012.03.009	journal	July 2012
Explicit and implicit methods for probabilistic common-cause failure analysis Wang, Chaonan; Xing, Liudong; Levitin, Gregory Reliability Engineering & System Safety, Vol. 131 https://doi.org/10.1016/j.ress.2014.06.024	journal	November 2014
A general cause based methodology for analysis of common cause and dependent failures in system risk and reliability assessments O’Connor, Andrew; Mosleh, Ali Reliability Engineering & System Safety, Vol. 145 https://doi.org/10.1016/j.ress.2015.06.007	journal	January 2016
Maximum likelihood and Bayesian inference for common-cause of failure model Nguyen, H. D.; Gouno, E. Reliability Engineering & System Safety, Vol. 182 https://doi.org/10.1016/j.ress.2018.10.003	journal	February 2019
A common cause failure model for components under age-related degradation Zhou, Taotao; Droguett, Enrique López; Modarres, Mohammad Reliability Engineering & System Safety, Vol. 195 https://doi.org/10.1016/j.ress.2019.106699	journal	March 2020
Spurious activation and operational integrity evaluation of redundant safety instrumented systems Qi, Meng; Kan, Yufeng; Li, Xun Reliability Engineering & System Safety, Vol. 197 https://doi.org/10.1016/j.ress.2019.106785	journal	May 2020
Bayesian inference for Common cause failure rate based on causal inference with missing data Nguyen, H. D.; Gouno, E. Reliability Engineering & System Safety, Vol. 197 https://doi.org/10.1016/j.ress.2019.106789	journal	May 2020
A discrete-time Bayesian network approach for reliability analysis of dynamic systems with common cause failures Guo, Yongjin; Zhong, Mingjun; Gao, Chao Reliability Engineering & System Safety, Vol. 216 https://doi.org/10.1016/j.ress.2021.108028	journal	December 2021
An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties Mi, Jinhua; Lu, Ning; Li, Yan-Feng Reliability Engineering & System Safety, Vol. 220 https://doi.org/10.1016/j.ress.2021.108295	journal	April 2022
A Redundancy-Guided Approach for the Hazard Analysis of Digital Instrumentation and Control Systems in Advanced Nuclear Power Plants Shorthill, Tate; Bao, Han; Zhang, Hongbin Nuclear Technology, Vol. 208, Issue 5 https://doi.org/10.1080/00295450.2021.1957659	journal	November 2021
Lessons Learned from the U.S. Nuclear Regulatory Commission’s Digital System Risk Research Arndt, Steven A.; Kuritzky, Alan Nuclear Technology, Vol. 173, Issue 1 https://doi.org/10.13182/NT11-A11478	journal	January 2011

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