骆正山,赵乐新,王小完.基于动态贝叶斯网络的海底管道点蚀疲劳损伤失效模型研究[J].表面技术,2020,49(1):269-275.
LUO Zheng-shan,ZHAO Le-xin,WANG Xiao-wan.Failure Model for Pitting Fatigue Damaged Pipeline of Subsea Based on Dynamic Bayesian Network.Surface Technology,2020,49(1):269-275.
基于动态贝叶斯网络的海底管道点蚀疲劳损伤失效模型研究
Failure Model for Pitting Fatigue Damaged Pipeline of Subsea Based on Dynamic Bayesian Network
投稿时间:2019-04-15  修订日期:2020-01-20
DOI:10.16490/j.cnki.issn.1001-3660.2020.01.032
中文关键词:  海底管道  点蚀  腐蚀疲劳  动态贝叶斯网络  失效模型
英文关键词:subsea pipelines  pitting  corrosion-fatigue  dynamic Bayesian network  failure model
基金项目:国家自然科学基金资助项目(41877527);陕西省社科基金资助项目(2018S34)
作者单位
骆正山 西安建筑科技大学 管理学院,西安 710055 
赵乐新 西安建筑科技大学 管理学院,西安 710055 
王小完 西安建筑科技大学 管理学院,西安 710055 
AuthorInstitution
LUO Zheng-shan School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China 
ZHAO Le-xin School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China 
WANG Xiao-wan School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China 
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中文摘要:
      目的 研究海底管道在点腐蚀和腐蚀疲劳双重影响下的整个破坏过程,基于动态贝叶斯网络构建系统失效模型,对海底管道系统不同疲劳寿命下的失效概率进行预测。方法 将点蚀疲劳损伤过程分为腐蚀点成核、腐蚀坑增长、短裂纹扩展和长裂纹扩展四个阶段,采用蒙特卡洛模拟方法对腐蚀点形成到短裂纹发生前的管道破坏过程进行分析,结合疲劳裂纹扩展的动态贝叶斯网络结构图,在充分考虑相关影响因素不确定性的基础上,为海底点蚀管道系统提出一种创新性的概率分析方法,对点蚀管道疲劳寿命的失效概率进行科学预测。结果 结合实例分析,通过蒙特卡洛模拟方法,求解得出腐蚀坑增长转变为短裂纹扩展状态的临界裂纹尺寸为0.8 mm。采用动态贝叶斯网络分析方法,对未经受维修保养的点蚀管道进行疲劳寿命预测,当管道运行到第35年时将会面临失效风险。结论 所构建的模型可以对海底点蚀管道腐蚀疲劳寿命失效概率进行合理预测,通过观测相关影响参数的变化,及时更新预测结果,有助于为海底管道系统制定有效的维修策略。
英文摘要:
      The work aims to study the whole failure process of submarine pipeline under the dual effects of pitting corrosion and corrosion fatigue, and construct a system failure model based on dynamic Bayesian network to predict the failure probability of submarine pipeline system under different fatigue life. The pitting fatigue damage process was divided into four stages: pit nucleation, pit growth, short and long crack growth. The Monte Carlo simulation method was used to analyze the pipeline failure process from pitting formation to short crack occurrence. Based on the dynamic Bayesian network structure diagram of fatigue crack growth and the uncertainties of related factors, an innovative probability analysis method for submarine pitting pipeline system was proposed to scientifically predict the failure probability of pitting pipeline fatigue life. Combining with the example analysis, the critical crack size of pit growth to short crack growth was 0.8 mm, which was solved by the Monte Carlo simulation method. The fatigue life of pitting pipeline without maintenance was predicted by the dynamic Bayesian network analysis method. The pipeline would face failure risk after 35 years of working. The results show that the model can reasonably predict the failure probability of corrosion-fatigue life of subsea pitting pipelines. By observing the changes of relevant influencing parameters and updating the predicted results in time, it is helpful to formulate effective maintenance strategies for subsea pipeline systems.
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