A novel RUL prognosis methodology of multilevel system with cascading failure: Subsea oil and gas transportation systems as a case study
A novel RUL prognosis methodology of multilevel system with cascading failure: Subsea oil and gas transportation systems as a case study
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具有级联故障的多级系统的新型 RUL 预测方法：以海底油气输送系统为例
DOI：
10.1016/j.oceaneng.2021.110141
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发表时间：
2021-12
影响因子：
5
通讯作者：
Ren Yi
中科院分区：
文献类型：
--
作者：
Cai Baoping;Shao Xiaoyan;Yuan Xiaobing;Liu Yonghong;Chen Guoming;Feng Qiang;Liu Yiqi;Ren Yi
级联故障对多电平系统的运行有很大的负面影响。虽然这类故障发生的频率比一般故障低，但它可能会造成停电和相当大的人员和经济损失。提出了一种基于位置重要度和功能重要度的多级系统级联故障动态贝叶斯网络建模方法，并对考虑级联故障的多级系统的剩余使用寿命进行了估计。通过检测节点的工作状态，确定节点的位置和功能重要性，计算下一层相关节点的性能。通过迭代计算到最后一层，最终确定故障节点的个数，并对多级系统的整体性能进行评估。以一个三级网络、每级4个节点的海底运输系统为例，验证了该方法的应用，并分析了该方法的可行性。结果表明，考虑连锁故障时，系统的RUL显著降低，这与非连锁故障模式的退化有很大不同。
Cascading failure has a great negative impact on the operation of multilevel systems. Although the frequency of this kind of failure is lower than that of general failure, it may cause outage and considerable human and economic losses. In this paper, a novel modeling methodology of cascading failure based on position importance and function importance is proposed by using dynamic Bayesian networks, and the remaining useful life (RUL) of multilevel systems considering cascading failure is estimated. By detecting the working state of the nodes, the position and function importance of the nodes are determined, and the performance of the next layer of related nodes is calculated. Through calculating iteratively to the last level, the number of failure nodes is finally determined, and the overall performance of the multilevel systems is evaluated. A subsea transportation system with three-level network and four nodes in each level is used to demonstrate the application of the proposed methodology, and the feasibility of the methodology is analyzed. The results show that the RUL of the systems is significantly reduced when considering the cascading failure, which is quite different from the degradation of the non-cascading failure mode.
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影响因子：
8.1
作者：
Fu, Xiuwen;Yao, Haiqing;Yang, Yongsheng
通讯作者：
Yang, Yongsheng
影响因子：
4.8
作者：
Fu, Xiuwen;Yao, Haiqing;Yang, Yongsheng
通讯作者：
Yang, Yongsheng
影响因子：
7.7
作者：
Wuzhao Yan;Bin Zhang;Guangquan Zhao;J. Weddington;Guangxing Niu
通讯作者：
Wuzhao Yan;Bin Zhang;Guangquan Zhao;J. Weddington;Guangxing Niu
影响因子：
8.4
作者：
Tobon-Mejia, D. A.;Medjaher, K.;Zerhouni, N.
通讯作者：
Zerhouni, N.
影响因子：
2.9
作者：
Benjamin Schäfer;Benjamin Schäfer;G. Yalcin
通讯作者：
Benjamin Schäfer;Benjamin Schäfer;G. Yalcin