Research on Human Error Risk Evaluation Using Extended Bayesian Networks with Hybrid Data

Pan, Xing; Zuo, Dujun; Zhang, Wenjin; Hu, Lunhu; Wang, Huixiong; Jiang, Jing

doi:10.1016/j.ress.2020.107336

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…where HEP is the actual mean HEP value given by Equation ( 7); and HEP s min is the minimum HEP value that could be achieved if the PSF s were always at their best level (N s ), with the lowest multiplier (M min s ). HEP s min is determined by evaluating Equation ( 7) for conditional probabilities when M s ¼ M min s , as indicated by Equations ( 13) and (14).…”

Section: Psf Importance Measuresmentioning

confidence: 99%

“…The dependencies between the variables of the parent and child nodes are quantified using conditional probability tables (CPTs). 14,20 The BN assessment includes both direct and reverse evaluations.…”

Section: Bayesian Networkmentioning

confidence: 99%

“…Several HRA studies 11–15 have utilized Bayesian networks (BNs) as tools to assist the analysis of human error. The BN‐HRA technique is a combined BN and HRA 16 .…”

Section: Introductionmentioning

confidence: 99%

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Importance measures for performance shaping factors of human reliability analysis

Albuquerque

Oliva

Saldanha

et al. 2022

Process Safety Progress

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The crew of a nuclear-powered submarine under development in Brasil plays an important role in its safety. For this reason, crew members must be trained in procedures against undesired events. However, information that would help to accurately decrease human error probability (HEP) on board nuclear-powered submarines is not available in the literature. Therefore, a methodology is required to obtain this information. This study introduces importance measures to evaluate the contribution of performance shaping factors (PSFs) and their levels to HEP. Importance measures Fussell-Vesely, risk reduction worth, risk achievement worth, and Birnbaum importance, commonly used in probabilistic safety assessment, are introduced to identify PSFs and PSF levels that contribute the most to HEP. The proposed approach is focused on identifying HEP's main contributors, oriented toward decision-making.The methodology uses the PSFs established in the Standardized Plant Analysis Risk-Human Reliability Analysis method and a Bayesian network to quantify HEP. Moreover, the results of an application case demonstrate the method's effectiveness in integrating expert opinions into representative values of HEP and in identifying critical PSFs and PSF levels that should be the object of the decision-making process to improve human performance.

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Section: Psf Importance Measuresmentioning

confidence: 99%

Section: Bayesian Networkmentioning

confidence: 99%

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Importance measures for performance shaping factors of human reliability analysis

Albuquerque

Oliva

Saldanha

et al. 2022

Process Safety Progress

View full text Add to dashboard Cite

show abstract

“…5 Now, two generations of the HRA methods have been established (the third generation is under development). To reduce the accidents linked with human errors, they have been extensively utilized in various industries, including nuclear, [6][7][8] aerospace, 9 chemical, 10 healthcare, 11 and other safety-critical industries. 12 The first phase of the HRA was developed roughly from the late 1970s to the early 1990s.…”

Section: Introductionmentioning

confidence: 99%

An HFM‐CREAM model for the assessment of human reliability and quantification

Lin

Huang

2022

Quality & Reliability Eng

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Human reliability analysis (HRA) is a proactive approach to model and evaluate systematic human errors and has been extensively implemented in various complicated systems. The assessment of human errors relies heavily on the knowledge and experience of experts in real‐world cases. Moreover, there are usually specific sorts of uncertainty while experts use linguistic labels to evaluate human failure events. In this context, this paper seeks to establish a new model based on the hesitant fuzzy matrix (HFM) and the cognitive reliability and error analysis method (CREAM) to conduct a quantitative analysis of human errors. This model handles the multiple crisp scores of the common performance conditions (CPCs) given by experts according to the context description in terms of CPCs, determines the weights of CPCs by the HFM, and elicits the human error probability (HEP) point estimation formula considering consequences based on the CREAM. Finally, the effectiveness and practicality of the presented HFM‐CREAM model are demonstrated through the emergency response analysis of the steam generator tube rupture (SGTR) in nuclear power plant.

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“…It can be used to automatically construct decision support systems and is used for inferring possible causal relations under certain conditions [4]; the edges in the BN graph have causal semantics. BN has been used widely in reliability analysis [5,6], medical diagnosis [7,8], gene analysis [9], fault diagnosis [10], language recognition [11], and index sensitivity analysis problems [12].…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Programming Strategy of Bayesian Network Structure Learning

et al. 2022

Scientific Programming

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Bayesian network structure learning based on dynamic programming strategy can be used to find the optimal graph structure compared with approximate search methods. The traditional dynamic programming method for Bayesian network structure learning is a depth-first-based strategy, which is inefficient. We proposed two methods to solve this problem. First, the dependency constraints were used to prune the process of calculating redundancy scores. The constraints were obtained by the conditional independence test from the observed data sets. However, it was difficult to guarantee the accuracy of the constraints, which may have led to a decrease in the accuracy of the method. Second, we proposed a breadth-first-based strategy, which enhanced efficiency greatly while also ensuring global optimality. Experimental results showed that on the standard network data sets, compared with the dynamic programming based on depth-first search (DFSDP) algorithm, dynamic programming based on constraints (CBDP) could reduce the average running time by 57.10% and that dynamic programming based on breadth-first search (BFSDP) could reduce the average running time by 50.02%. On the UCI data sets, compared with DFSDP, CBDP reduced the average running time by 40.71%, and BFSDP reduced the average running time by 81.78%.

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Research on Human Error Risk Evaluation Using Extended Bayesian Networks with Hybrid Data

Cited by 10 publications

References 40 publications

Importance measures for performance shaping factors of human reliability analysis

Importance measures for performance shaping factors of human reliability analysis

An HFM‐CREAM model for the assessment of human reliability and quantification

Research on Dynamic Programming Strategy of Bayesian Network Structure Learning

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