System Reliability Analysis for Independent and Nonidentical Components based on Survival Signature

Probabilistic Engineering Mechanics, 2023

引用方式: Zheng, Y., Zhang, Y., Lin, J. (2023). System Reliability Analysis for Independent and Nonidentical Components based on Survival Signature. Probabilistic Engineering Mechanics, 73, 103466. doi: 10.1016/j.probengmech.2023.103466 cited by count


Survival signature已广泛用于分析具有故障事件的任意依赖系统结构。 当存在不同的组件时,研究系统可靠性具有挑战性,因为生存特征的基本前提是组件应该在同一子系统内互换。 在这项研究中,提出了两种基于基本Survival signature思想的方法来检查具有独立但不一定同分布(INID)组件的复杂系统的可靠性。 第一种算法基于加权随机抽样(WRS)方法来计算Survival signature, 第二种算法在计算概率结构时采用了分而治之的思想。 本研究详细介绍了这两种算法及其在示例系统中的应用。 结果表明,与传统方法相比,这两种算法可以显著减少计算时间。最后,将所开发的算法用于实际问题——清华校园供水管道系统的可靠性分析,并结合预测结果对系统可靠性及其组件的重要性进行了讨论。

Survival signatures have been widely used for analyzing arbitrary dependent system structures having failure events. It is challenging to study system reliability when diverse components are present since the fundamental premise of the survival signatures is that components should be interchangeable within the same subsystem. In this research, two methods based on the fundamental survival signature idea are suggested to examine the reliability of complex systems with independent but not necessarily identically distributed (INID) components. The first algorithm is based on the weighted random sampling (WRS) method to calculate the survival signatures. The second algorithm adopts the idea of divide-and-conquer in computing the probability structure. The application of these two algorithms and the exhaustive algorithm in analyzing the example systems are presented. The results show that the two algorithms can significantly reduce the computation time compared to the traditional methods. Finally, the developed algorithms are used in the reliability analysis of a real practical problem, the Tsinghua campus water supply pipeline system. The results regarding the predictions of system reliability as well as the component importance index are discussed.



The authors gratefully acknowledge the financial support from National Natural Science Foundation of China under project number of Grand No. 51908324&52111540161. The support from Tsinghua University Initiative Scientific Research Program (20213080003) is also greatly appreciated.