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给定一组不确定输入和极限状态函数时，计算工程系统的失效概率。主动学习方案已被证明是一种可行工具，但目前其在系统可靠性分析中的效率仍不及元件可靠性分析领域。这是由于系统问题的一些特性所致，例如存在多个失效模式及其对失效贡献的不均衡性，或对系统配置（如串联或并联）的依赖性。本文提出一种新颖的主动学习策略，专为解决一般系统可靠性问题而设计。该算法结合了子集模拟与克里金/主成分克里金法，并依赖于针对此类方法弱点的定制化富集方案。具体而言，它包含三个组成部分：(i) 无需指定系统配置的新学习函数；(ii) 可自动检测不同失效模式的基于密度的聚类技术；(iii) 用于估计每个极限状态对系统失效贡献的敏感性分析，从而仅选择最相关的极限状态进行富集。通过两个解析算例对所提方法进行验证，并与文献结果进行对比。最终解决了与电力传输相关的复杂工程问题，展示了该方法在实际场景中的高效性。