贝叶斯网络
海底
正确性
变阶贝叶斯网络
贝叶斯概率
计算机科学
图表
可靠性工程
软件
流程图
数据挖掘
工程类
人工智能
贝叶斯推理
算法
控制工程
海洋工程
数学
统计
程序设计语言
作者
Baoping Cai,Yonghong Liu,Zengkai Liu,Xinliang Tian,Yanzhen Zhang,Renjie Ji
出处
期刊:Risk Analysis
[Wiley]
日期:2012-10-29
卷期号:33 (7): 1293-1311
被引量:96
标识
DOI:10.1111/j.1539-6924.2012.01918.x
摘要
This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three‐axiom‐based analysis partially validates the correctness and rationality of the proposed Bayesian network model.
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