Abstract
Human errors contribute a significant portion of risk in safety critical applications and methods for estimation of human error probability have been a topic of research for over a decade. The scarce data available on human errors and large uncertainty involved in the prediction of human error probabilities make the task difficult. This paper presents a Bayesian belief network (BBN) model for human error probability estimation in safety critical functions of a nuclear power plant. BBN is a powerful tool and has been widely used for risk and reliability analysis framework under the conditions of uncertainty. BBNs are joint probability distribution between multiple variables (i.e., performance shaping factors, error modes, error mechanisms, etc.) expressed as directed acyclic graphs consisting of nodes and arcs. Nodes represent system components, and arc represents relationship among them with conditional probability tables. This network functions as an engine for calculation of probability of events given the observation of other events in the same network. BBN can be used to model the uncertainty parameters in a system. Sensitivity analysis can also be performed to study how uncertainty in the model output can be attributed to different sources of uncertainty in the model input. It is frequently applied in real-world situations such as diagnosis, forecasting, and environment, but received less attention in the area of human reliability. In view of its natural architecture, BBN is found to be more appropriate when there is scarce, multi-source data available as in the case of human error data. Further, the probabilistic approach of BBN is best suited for safety assessment to predict system reliability and estimate the probability of consequence of an event. BBN has been adapted to model human factors by taking into account the different parameters and their mutual influences. BBN can also be used to identify the potential human factors leading to significant reduction of accident probability during the operation of NPP. Several researchers have developed advanced HRA models to estimate the error probability but most of the HRA model requires expert judgment at several stages and hence it is subjective. Other models such as HCR and CREAM are not applicable for all scenarios. Limited studies are carried out to eliminate the need of human expert in the human reliability methods. This paper introduces a method that uses the available historical data and provides efficient technique for automated evaluation of human error probability using BBN. In this respect, human cognitive reliability (HCR) has been identified as a suitable model to estimate the mean time required for human intervention and in the selection of human action to estimate HEP in the model. The developed model using BBN would help to estimate HEP with limited human intervention. A step-by-step illustration of the application of the method and subsequent evaluation is provided with a relevant case study and the model is expected to provide useful insights into risk assessment studies.
Similar content being viewed by others
References
Baraldi Piero, Podofillini Luca, Mkrtchyan Lusine, Zio Enrico, Dang Vinh N (2015) Comparing the treatment of uncertainty in bayesian networks and fuzzy expert systems used for a human reliability analysis application. Reliab Eng Syst Saf 138:176–193
Bouissou M, Martin F, Ourghanlian A (1999) Assessment of a safety-critical system including software: a bayesian belief network for evidence sources. In: Annual Reliability and Maintainability Symposium, 1999 Proceedings (Cat. No.99CH36283). pp. 142–50
Cai Baoping et al (2012) Using bayesian networks in reliability evaluation for subsea blowout preventer control system. Reliab Eng Syst Saf 108:32–41
Dai Licao, Li Zhang, Li Pengcheng (2011) HRA in China: model and data. Saf Sci 49(3):468–472
Dolby AJ (1990) A comparison of operator response times predicted by the hcr model with those obtained from simulators. Int J Qual Reliab Manag 7(5):19–26
Droguett EL, das Chagas Moura M, Jacinto CM, Silva MF (2008) A semi-Markov model with Bayesian belief network based human error probability for availability assessment of downhole optical monitoring systems. Simul Model Pract Theory 16(10):1713–1727
Embrey DE, Humphreys P, Rosa EA, Kirwan B, Rea K (1984) SLIM-MAUD: an approach to assessing human error probabilities using structured expert judgment. Volume II. Detailed analysis of the technical issues, vol 2. NUREG/CR-3518. Brookhaven National Laboratory, Upton
Forester J, Kolaczkowski A, Cooper S, Bley D, Lois E (2007) ATHEANA User’s guide final report (NUREG-1880)
Francis Royce A, Guikema Seth D, Henneman Lucas (2014) Bayesian belief networks for predicting drinking water distribution system pipe breaks. Reliab Eng Syst Saf 130:1–11
Gehl P, D’Ayala D (2016) Development of bayesian networks for the multi-hazard fragility assessment of bridge systems. Struct Saf 60:37–46
Gertman D, Blackman H, Byers J, Haney L, Smith C, Marble J (2005) The SPAR-H method. NUREG/CR-6883. US Nuclear Regulatory Commission, Washington, DC
Griffith Candice D, Mahadevan Sankaran (2015) Human reliability under sleep deprivation: derivation of performance shaping factor multipliers from empirical data. Reliab Eng Syst Saf 144:23–34
Groth K, Mosleh A (2010) A performance shaping factors causal model for nuclear power plant human reliability analysis. In: 10th international probabilistic safety assessment and management conference
Groth Katrina M, Swiler Laura P (2013) Bridging the gap between HRA research and HRA practice: a bayesian network version of SPAR-H. Reliab Eng Syst Saf 115:33–42
Hallbert B (2007) The employment of empirical data and bayesian methods in human reliability analysis: a feasibility study (Electronic Resource) NUREG/CR-6949
Hannaman GW, Spurgin AJ, Lukic Y (1985) A model for assessing human cognitive reliability in PRA studies. In: conference record for 1985 IEEE third conference on human factors and nuclear safety
Hollnagel E (1998) Cognitive reliability and error analysis method. Elsevier, Oxford
Jitwasinkul Bhanupong, Hadikusumo Bonaventura H W, Memon Abdul Qayoom (2016) A bayesian belief network model of organizational factors for improving safe work behaviors in thai construction industry. Saf Sci 82:264–273
Kang C (1999) A bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems. Expert Syst Appl 17(1):21–32
Kim Jaewhan, Park Jinkyun (2012) Reduction of test and maintenance human errors by analyzing task characteristics and work conditions. Prog Nucl Energy 58:89–99
Kim Sa Kil, Lee Yong Hee, Jang Tong Il, Oh Yeon Ju, Shin Kwang Hyeon (2014) An investigation on unintended reactor trip events in terms of human error hazards of korean nuclear power plants. Ann Nucl Energy 65:223–231
Lee Eunchang, Park Yongtae, Shin Jong Gye (2009) Large engineering project risk management using a bayesian belief network. Expert Syst Appl 36(3):5880–5887
Liao Huafei, Groth Katrina, Stevens-Adams Susan (2015) Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method. Reliab Eng Syst Saf 144:159–169
Liu Kang-Hung, Hwang Sheue-Ling (2014) Human performance evaluation: the procedures of ultimate response guideline for nuclear power plants. Nucl Eng Des 273:234–240
Martins Marcelo Ramos, Maturana Marcos Coelho (2013) Application of bayesian belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents. Reliab Eng Syst Saf 110:89–109
Mkrtchyan L, Podofillini L, Dang VN (2015) Bayesian belief networks for human reliability analysis: a review of applications and gaps. Reliab Eng Syst Saf 139:1–5
Mkrtchyan L, Podofillini L, Dang VN (2016) Methods for building conditional probability tables of bayesian belief networks from limited judgment: an evaluation for human reliability application. Reliab Eng Syst Saf 151:93–112
Mosleh Ali (1992) Bayesian modeling of expert-to-expert variability and dependence in estimating rare event frequencies. Reliab Eng Syst Saf 38(1–2):47–57
Park Kyung S, in Lee J (2008) A new method for estimating human error probabilities: AHP-SLIM. Reliab Eng Syst Saf 93(4):578–587
Park Jinkyun, Kim Yochan, Kim Jung Han, Jung Wondea, Jang Seung Cheol (2015) Estimating the response times of human operators working in the main control room of nuclear power plants based on the context of a seismic event—a case study. Ann Nucl Energy 85:36–46
Pasquale Di, Valentina Salvatore Miranda, Iannone Raffaele, Riemma Stefano (2015) A simulator for human error probability analysis (SHERPA). Reliab Eng Syst Saf 139:17–32
Podofillini L, Dang VN (2013) A bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction. Reliab Eng Syst Saf 117:52–64
Prasad Mahendra, Gaikwad AJ (2015) Human error probability estimation by coupling simulator data and deterministic analysis. Prog Nucl Energy 81:22–29
Sharma SK (2008) Human reliability analysis : a compendium of methods, data and event studies for nuclear power plants (TEC. DOC. NO. AERB/NPP/TD/O-2). Atomic Energy Regulatory Board, Mumbai
Swain AD (1987) Accident sequence evaluation program. NUREG/CR-4772. US Nuclear Regulatory Commission, Washington, DC
Swain AD, Guttmann HE (1983) Handbook of human reliability analysis with emphasis on nuclear power plant applications (NUREG/CR-1278). US Nuclear Regulatory Commission, Rockville, p 728
Taylan Osman, Bafail Abdallah O, Abdulaal Reda M S, Kabli Mohammed R (2014) Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Appl Soft Comput J 17:105–116
Trucco P, Leva MC (2007) A probabilistic cognitive simulator for HRA studies (PROCOS). Reliab Eng Syst Saf 92(8):1117–1130
Trucco P, Cagno E, Ruggeri F, Grande O (2008) A bayesian belief network modelling of organisational factors in risk analysis: a case study in maritime transportation. Reliab Eng Syst Saf 93(6):823–834
Tu J, Lin W, Lin Y (2015) A Bayes-SLIM based methodology for human reliability analysis of lifting operations. Int J Ind Ergon 45:48–54
Yang Zili, Wang Jin, Rochdi Merzouki (2011) Bayesian modelling for human error probability analysis in CREAM. 2011 Int Conf Qual Reliab Risk Maint Saf Eng 5:137–142
Zhang Li, He Xuhong, Dai Li Cao, Huang Xiang Rui (2007) The simulator experimental study on the operator reliability of qinshan nuclear power plant. Reliab Eng Syst Saf 92(2):252–259
Acknowledgement
The authors are grateful to Shri. V. Balasubramaniyan, Director, SRI, AERB for his constant encouragement and support to carry out this research work and Dr. L. Thilagam, Technical Officer, SRI, AERB for helping the coding process in this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Karthick, M., Senthil Kumar, C. & Paul Robert, T. BAYES-HEP: Bayesian belief networks for estimation of human error probability. Life Cycle Reliab Saf Eng 6, 187–197 (2017). https://doi.org/10.1007/s41872-017-0026-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41872-017-0026-4