A global sensitivity analysis approach for multiple failure modes based on convex-probability hybrid uncertainty

Abstract

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.

Matlab sensitivity analysis toolbox: an application on faults identification in induction motors

“…Hence, the exact purpose of these tools is to prioritize the important data and eliminate the trivial data to optimize the system and reduce over-design (Cacuci et al, 2005). Thus, a sensitivity analysis (SA) is used in different sciences and engineering fields (Toufiq and Hadid, 2018;Ranjbar and Saffar, 2016;Tian, 2012;Zhang et al, 2020aZhang et al, , 2020bAbbiati et al, 2021). In general, the SA methods are divided into local and global methods; the first category is known as one-at-a-time methods, i.e.…”

“…In general, the SA methods are divided into local and global methods; the first category is known as one-at-a-time methods, i.e. SA is calculated according to only one changeable factor at each time, unlike the global methods (Tian, 2012;Zhang et al, 2020aZhang et al, , 2020b. Although the local methods are easier and require less simulation time than the global methods, they are only applicable for linear systems and they can be interrupted in local variations (Iooss and Lemaître, 2015;Tian, 2012).…”