Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies

2021 ◽  
pp. 107898
Author(s):  
Haihe Li ◽  
Pan Wang ◽  
Xiaoyu Huang ◽  
Zheng Zhang ◽  
Changcong Zhou ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Importance Measure ◽  
Parametric Sensitivity ◽  
Parametric Sensitivity Analysis ◽  
Vine Copula

scholarly journals Parametric Sensitivity Analysis for Importance Measure on Failure Probability and Its Efficient Kriging Solution

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yishang Zhang ◽  
Yongshou Liu ◽  
Xufeng Yang
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Computational Effort ◽  
Importance Measure ◽  
Sensitivity Index ◽  
Reliability Engineering ◽  
Parametric Sensitivity ◽  
Distribution Parameters ◽  
The Moment ◽  
Sensitivity Indicator

The moment-independent importance measure (IM) on the failure probability is important in system reliability engineering, and it is always influenced by the distribution parameters of inputs. For the purpose of identifying the influential distribution parameters, the parametric sensitivity of IM on the failure probability based on local and global sensitivity analysis technology is proposed. Then the definitions of the parametric sensitivities of IM on the failure probability are given, and their computational formulae are derived. The parametric sensitivity finds out how the IM can be changed by varying the distribution parameters, which provides an important reference to improve or modify the reliability properties. When the sensitivity indicator is larger, the basic distribution parameter becomes more important to the IM. Meanwhile, for the issue that the computational effort of the IM and its parametric sensitivity is usually too expensive, an active learning Kriging (ALK) solution is established in this study. Two numerical examples and two engineering examples are examined to demonstrate the significance of the proposed parametric sensitivity index, as well as the efficiency and precision of the calculation method.

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