Reliability analysis with multiple failure modes is needed because more than one failure mode exists in many engineering applications. Kriging-based surrogate model is widely adopted for component reliability analysis because of its high computational efficiency. Compared with Kriging-based component reliability analysis, selecting the sample points that affect the system performance is more difficult than that of a single failure mode in system reliability analysis. Therefore, how to select suitable sample points is a key problem in system reliability analysis. Meanwhile, reducing the number of calls to the performance functions is challenging, especially for systems with time-consuming performance functions. In this paper, an improved Kriging-based system reliability analysis approach is proposed based on the two strategies. In strategy 1, the initial sample points are determined by considering only two different cases: (a) the candidate samples are selected from the safe regions only for series systems; (b) the candidate samples are selected from the failure regions only for parallel systems. Therefore, samples having little contributions to the composite performance function are avoided. In strategy 2, the sample points determined in strategy 1 will be further optimized by interpolating. From comparisons with three reported methods in numerical examples, the efficiency and accuracy of the proposed method are illustrated.

因为在许多工程应用中存在不止一种故障模式，所以需要具有多种故障模式的可靠性分析。基于克里格模型的替代模型由于具有较高的计算效率而被广泛用于组件可靠性分析。与基于Kriging的组件可靠性分析相比，在系统可靠性分析中，选择影响系统性能的采样点比单一故障模式要困难得多。因此，如何选择合适的采样点是系统可靠性分析中的关键问题。同时，减少对性能函数的调用数量具有挑战性，特别是对于具有耗时性能函数的系统而言。本文基于这两种策略，提出了一种改进的基于Kriging的系统可靠性分析方法。在策略1中，初始采样点仅通过考虑两种不同情况来确定：（a）仅从串联系统的安全区域中选择候选样本；（b）仅从并行系统的失效区域中选择候选样本。因此，避免了对复合性能函数贡献很小的样品。在策略2中，将通过插值进一步优化在策略1中确定的采样点。通过在数值示例中与三种报告方法的比较，说明了该方法的效率和准确性。避免对复合性能函数贡献很小的样本。在策略2中，将通过插值进一步优化在策略1中确定的采样点。通过在数值示例中与三种报告方法的比较，说明了该方法的效率和准确性。避免对复合性能函数贡献很小的样本。在策略2中，将通过插值进一步优化在策略1中确定的采样点。通过在数值示例中与三种报告方法的比较，说明了该方法的效率和准确性。