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首页> 外文期刊>Journal of loss prevention in the process industries >Risk analysis of subsea blowout preventer by mapping GO models into Bayesian networks
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Risk analysis of subsea blowout preventer by mapping GO models into Bayesian networks

机译：将Go模型映射到贝叶斯网络的风险分析

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Bayesian network (BN) is commonly used in probabilistic risk quantification due to its powerful capacity in uncertain knowledge representation and uncertainty reasoning. For the formalization of BN models, this paper presents a novel approach on constructing a BN from GO model. The equivalent BNs of the seventeen basic operators in GO methodology are developed. Therefore, the existing GO model can be mapped into an equivalent BN on basis of these developed BNs of the operators. Subsea blowout preventer (BOP) system plays an important role in providing safety during the subsea drilling activities. A case of closing the subsea BOP in the presence of pump failures is used to illustrate the mapping process. First, its GO model is presented according to the flow chart of the case. Then, BN is obtained based on the presented GO model. The developed BN relaxes the limitations of GO model and is capable of probability updating and probability adapting. Sensitivity analysis is performed to find the key influencing factor. The three-axiom-based analysis method is used to validate the developed BN.
机译：由于其不确定知识表示和不确定性推理，贝叶斯网络（BN）常用于概率风险量化。对于BN模型的形式化，本文提出了一种从GO模型构建BN的新方法。开发了GO方法中的十七个基本运算符的等效BNS。因此，现有的GO模型可以基于操作员的这些开发的BNS映射到等同的BN。海底井喷预防员（BOP）系统在海底钻井活动期间提供了重要作用。在存在泵故障存在下关闭海底BOP的情况用于说明映射过程。首先，通过壳体的流程图呈现其GO模型。然后，基于所呈现的GO模型获得BN。开发的BN放宽了GO模型的局限性，并且能够更新和概率适应概率。执行敏感性分析以找到关键的影响因素。基于三轴的分析方法用于验证发达的BN。

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