System reliability analysis aims at computing the probability of failure of an engineering system given a set of uncertain inputs and limit state functions. Active-learning solution schemes have been shown to be a viable tool but as of yet they are not as efficient as in the context of component reliability analysis. This is due to some peculiarities of system problems, such as the presence of multiple failure modes and their uneven contribution to failure, or the dependence on the system configuration (e.g., series or parallel). In this work, we propose a novel active learning strategy designed for solving general system reliability problems. This algorithm combines subset simulation and Kriging/PC-Kriging, and relies on an enrichment scheme tailored to specifically address the weaknesses of this class of methods. More specifically, it relies on three components: (i) a new learning function that does not require the specification of the system configuration, (ii) a density-based clustering technique that allows one to automatically detect the different failure modes, and (iii) sensitivity analysis to estimate the contribution of each limit state to system failure so as to select only the most relevant ones for enrichment. The proposed method is validated on two analytical examples and compared against results gathered in the literature. Finally, a complex engineering problem related to power transmission is solved, thereby showcasing the efficiency of the proposed method in a real-case scenario.

翻译：系统可靠性分析旨在通过一组不确定性输入和极限状态函数计算工程系统的失效概率。主动学习方法已被证明是可行的工具，但至今其效率仍不及元件可靠性分析领域。这源于系统问题的若干特性，例如多个失效模式的存在及其对失效的不均匀贡献，或对系统配置（如串联或并联）的依赖性。本文提出一种专为求解通用系统可靠性问题而设计的新型主动学习策略。该算法融合了子集模拟与克里金/多项式混沌克里金法，并采用针对此类方法弱点而定制的富集方案。具体而言，该策略包含三个组成部分：（i）一种无需指定系统配置的新型学习函数；（ii）基于密度的聚类技术，可自动识别不同失效模式；（iii）通过灵敏度分析估计各极限状态对系统失效的贡献，从而仅筛选出最相关的状态进行富集。本方法通过两个解析算例进行验证，并与文献结果对比。最后，解决了一个与电力传输相关的复杂工程问题，从而在实际场景中展示了所提方法的有效性。