Human reliability analysis for manned submersible diving process based on CREAM and Bayesian network

Chen, Dengkai; Fan, Yu; Ye, Cong; Zhang, Shuai

doi:10.1002/qre.2501

Cited by 20 publications

(17 citation statements)

References 26 publications

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“…Golestani N et al [21] used hierarchical Bayesian networks to explain the causal relationships between environmental factors, human error patterns, and scenario-based activities. Chen et al [22] utilized CREAM algorithm to calculate the prior probability of each root node error on the basis of constructing the Bayesian network in the diving process.…”

Section: Introductionmentioning

confidence: 99%

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

Wang

et al. 2021

PLoS ONE

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Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

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Section: Introductionmentioning

confidence: 99%

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

Wang

et al. 2021

PLoS ONE

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“…Although some studies have examined human error and its effects, however, some issues have not yet been fully investigated. The main purpose of this paper is to clarify and to complete the following issues: Investigate the causes that affect the performance of personnel and their error.Previous research has focused on finding human factors influencing events, analyzing human errors in operational processes, and calculating the probability of error behavior 7 . Therefore, the cause of actual human errors has been identified for power transmission companies limitedly and generally and the potential causes have been less predicted. Study the factors affecting power industry maintenance groups.In most researches, the error of the power grid operators has been studied.…”

Section: Introductionmentioning

confidence: 99%

Estimating and ranking the impact of human error roots on power grid maintenance group based on a combination of mathematical expectation, Shannon entropy, and TOPSIS

Tavakoli

Nafar

2021

Quality & Reliability Eng

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Due to the importance of electrical grid reliability, analysis and evaluation of human error in the maintenance of electrical networks should be also considered seriously. The root causes of these errors must be identified and prioritized to plan for human error reduction. One of the objectives of the present study is to identify and predict these roots for power transmission maintenance groups from organizational, job position, communication, individual, and supervision aspects along with the relationships between these factors. In particular, this paper demonstrates that supervisor behavior as an external factor has a significant effect on maintenance personnel error. For this reason, special attention has been paid to identifying and controlling human factors from a supervisory point of view in this study. This paper also provides a method for detecting the extent of the expected influence of these roots on each personnel, since human error has a random nature. This is done based on the law of mathematical expectation. Finally, a method is suggested to rank roots based on greater effectiveness and evaluate personnel with higher error expectations. The proposed method is a combination of intermediate methods, Shannon entropy, and technique for order of preference by similarity to ideal solution (TOPSIS). The origins of the four human errors between 2014 and 2018 related to the two experts of Fars Electricity Maintenance Contractor Company are compared by the proposed method.

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“…Chen et al 20 used Bayesian network to overcome the insufficient data of factors and constructed the comprehensive CREAM to quantify the human reliability for space missions. Chen et al 21 adopted Bayesian network to calculate the posterior probabilities and developed an extended CREAM method to calculate the prior probability of human error. Pan et al 22 adopted a constant value α to reflect the uncertain information and proposed a mixed CREAM model to quantify the human error probability in HRA problem.…”

Section: Introductionmentioning

confidence: 99%

An extended CREAM model based on analytic network process under the type‐2 fuzzy environment for human reliability analysis in the high‐speed train operation

Chen

Qin

2020

Quality & Reliability Eng

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Human error is one of the largest contributing factors to unsafe operation and accidents in high‐speed train operation. As a well‐known second‐generation human reliability analysis (HRA) technique, the cognitive reliability and error analysis method (CREAM) has been introduced to address HRA problems in various fields. Nevertheless, current CREAM models are insufficient to deal with the HRA problem that need to consider the interdependencies between the Common Performance Conditions (CPCs) and determine the weights of these CPCs, simultaneously. Hence, the purpose of this paper is to develop a hybrid HRA model by integrating CREAM, the interval type‐2 fuzzy sets, and analytic network process (ANP) to overcome this drawback. Firstly, the interval type‐2 fuzzy sets are utilized to express the highly uncertain information of CPCs. Secondly, the ANP is incorporated into the CREAM to depict the interdependencies between the CPCs and determine their weights. Furthermore, human error probability (HEP) can be calculated based on the obtained weights. Finally, an illustrative example of the HRA problem in high‐speed train operation is proposed to demonstrate the application and validity of the proposed HRA model. The results indicate that experts prefer to express their preferences by fuzzy sets rather than crisp values, and the interdependences between the CPCs can be better depicted in the proposed model.

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Human reliability analysis for manned submersible diving process based on CREAM and Bayesian network

Cited by 20 publications

References 26 publications

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

Estimating and ranking the impact of human error roots on power grid maintenance group based on a combination of mathematical expectation, Shannon entropy, and TOPSIS

An extended CREAM model based on analytic network process under the type‐2 fuzzy environment for human reliability analysis in the high‐speed train operation

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