摘要
针对事故树分析法不能对管道风险进行多态性分析,且无法实现双向推理问题,提出一种基于贝叶斯网络的油田管道失效概率计算方法。首先,建立油田管道失效风险事故树模型,利用事故树与贝叶斯网络的转换确定贝叶斯网络结构,完成管道失效风险贝叶斯网络模型结构的构建;其次,考虑到由专家知识经验及期望最大化算法确定的网络参数存在较大的估计误差问题,引入遗传算法完成贝叶斯网络参数学习以此获取最优参数;最后,将该方法应用于实际油田管道风险问题,利用Ge NIe贝叶斯网络仿真软件计算油田管道失效概率,同时对每个风险因素进行分析并得到影响管道发生失效的致因链。实验证明,所提方法在评估精度上得到了显著地提升。
In view of the fact that the fault tree analysis method can not analyze the pipeline risk polymorphism,and can not realize two-way reasoning,a calculation method of pipeline failure probability based on Bayesian network is proposed.Firstly,the fault tree model of oil field pipeline failure risk is established,and the Bayesian network structure is determined by the transformation of fault tree and Bayesian network to complete the construction of the Bayesian network model structure of pipeline failure risk.Secondly,considering the large estimation error of network parameters determined by expert knowledge experience and expectation maximization algorithm,genetic algorithm is introduced to complete the Bayesian network structure.Finally,this method is applied to the actual risk problem of oil field pipeline,and the failure probability of oil field pipeline is calculated by using the genie Bayesian network simulation software.And each risk factor is analyzed and the cause chain affecting the pipeline failure is obtained.The experimental results show that the method proposed has significantly improved the evaluation accuracy.
作者
任伟建
于雪
霍凤财
康朝海
REN Weijian;YU Xue;HUO Fengcai;KANG Chaohai(School of Electrical Engineering and Information,Northeast Petroleum University,Daqing 163318,China;Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control,Northeast Petroleum University,Daqing 163318,China)
出处
《吉林大学学报（信息科学版）》
CAS
2021年第1期66-76,共11页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(6193307)
黑龙江省自然科学基金资助项目(F2018004)。
关键词
事故树分析
贝叶斯网络
参数学习
遗传算法
管道
fault tree analysis
bayesian network
parameter learning
genetic algorithm
pipeline
