Purpose

This paper aims to develop a method for evaluating the failure probability and global sensitivity of multiple failure modes based on convex-probability hybrid uncertainty.

Design/methodology/approach

The uncertainty information of the input variable is considered as convex-probability hybrid uncertainty. Moment-independent variable global sensitivity index based on the system failure probability is proposed to quantify the effect of the input variable on the system failure probability. Two-mode sensitivity indices are adopted to characterize the effect of each failure mode on the system failure probability. The method based on active learning Kriging (ALK) model with a truncated candidate regions (TCR) is adopted to evaluate the systems failure probability, as well as sensitivity index and this method is termed as ALK-TCR.

Findings

The results of five examples demonstrate the effectiveness of the sensitivity index and the efficiency of the ALK-TCR method in solving the problem of multiple failure modes based on the convex-probability hybrid uncertainty.

Originality/value

Convex-probability hybrid uncertainty is considered on system reliability analysis. Moment-independent variable sensitivity index based on the system failure probability is proposed. Mode sensitivity indices are extended to hybrid uncertain reliability model. An effective global sensitivity analysis approach is developed for the multiple failure modes based on convex-probability hybrid uncertainty.

中文翻译：

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基于凸概率混合不确定性的多失效模式全局灵敏度分析方法

目的

本文旨在开发一种基于凸概率混合不确定性评估多种失效模式的失效概率和全局灵敏度的方法。

设计/方法/方法

输入变量的不确定性信息被认为是凸概率混合不确定性。提出了基于系统故障概率的矩独立变量全局灵敏度指标来量化输入变量对系统故障概率的影响。采用两种模式灵敏度指标来表征每种故障模式对系统故障概率的影响。采用基于截断候选区域（TCR）的主动学习克里金（ALK）模型的方法来评估系统故障概率，以及灵敏度指标，该方法称为ALK-TCR。

发现

五个实例的结果证明了灵敏度指标的有效性和 ALK-TCR 方法在解决基于凸概率混合不确定性的多种失效模式问题中的有效性。

原创性/价值

系统可靠性分析考虑了凸概率混合不确定性。提出了基于系统故障概率的与力矩无关的可变灵敏度指标。模式敏感性指标被扩展到混合不确定可靠性模型。针对基于凸概率混合不确定性的多种失效模式，开发了一种有效的全局灵敏度分析方法。