An advanced method for the sensitivity analysis of safety system

Safety system always involves variation on account of the epistemic uncertainty of the inputs. Therefore, it is significant to identify the uncertainty source of the output for the safety system. Sensitivity analysis (SA) which measures the effect of variance variation of inputs on the absolute change in the variance of system unsafety is a useful tool for identifying the importance of inputs. A finite difference method has been proposed to estimate the effect in the existing work. This method may be numerical unstable or inaccuracy and is computational heavy. In order to overcome these issues, an advanced method which combing the simulation method and the analytical deduction of the partial derivative is established in this paper to estimate the SA indices. Discussion and several examples are introduced to illustrate the efficiency and accuracy of the proposed method when comparing with the finite difference method.

[1]  Zhonglai Wang,et al.  Reliability sensitivity analysis for structural systems in interval probability form , 2011 .

[2]  Kais Zaman,et al.  Likelihood-based representation of epistemic uncertainty and its application in robustness-based design optimization , 2017 .

[3]  Zhenzhou Lu,et al.  A modified importance sampling method for structural reliability and its global reliability sensitivity analysis , 2018 .

[4]  Wenrui Hao,et al.  Efficient sampling methods for global reliability sensitivity analysis , 2012, Comput. Phys. Commun..

[5]  Harry R. Millwater,et al.  Universal properties of kernel functions for probabilistic sensitivity analysis , 2009 .

[6]  Tao Chen,et al.  The effect of parameter uncertainty on achieved safety integrity of safety system , 2012, Reliab. Eng. Syst. Saf..

[7]  Hee Youb Kang,et al.  Application of maximum entropy principle for reliability-based design optimization , 2009 .

[8]  Yong Jiang,et al.  Parametric study and global sensitivity analysis for co-pyrolysis of rape straw and waste tire via variance-based decomposition. , 2018, Bioresource technology.

[9]  Z. Geem.  Optimal cost design of water distribution networks using harmony search , 2006 .

[10]  Zhenzhou Lu,et al.  A new method for evaluating Borgonovo moment-independent importance measure with its application in an aircraft structure , 2014, Reliab. Eng. Syst. Saf..

[11]  Samuel Kotz,et al.  The beta exponential distribution , 2006, Reliab. Eng. Syst. Saf..

[12]  Seonho Cho,et al.  Adjoint design sensitivity analysis of dynamic crack propagation using peridynamic theory , 2015 .

[13]  K. Choi,et al.  Sampling-based RBDO using the stochastic sensitivity analysis and Dynamic Kriging method , 2011 .

[14]  D. Cacuci,et al.  A Comparative Review of Sensitivity and Uncertainty Analysis of Large-Scale Systems—II: Statistical Methods , 2004 .

[15]  Ming Jian Zuo,et al.  An efficient method for evaluating the effect of input parameters on the integrity of safety systems , 2016, Reliab. Eng. Syst. Saf..

[16]  Ke Wang,et al.  The Structure Dynamic Optimum Design of the Frame for High Speed Conveying Manipulator Based on Sensitivity Analysis , 2013 .

[17]  Loïc Brevault,et al.  Probability of failure sensitivity with respect to decision variables , 2015 .

[18]  Jon C. Helton,et al.  Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal , 1993 .

[19]  Lu Zhenzhou,et al.  An entropy-based global sensitivity analysis for the structures with both fuzzy variables and random variables , 2013 .

[20]  J. L. Rouvroye.  Enhanced Markov analysis as a method to assess safety in the process industry , 2001 .

[21]  Emanuele Borgonovo,et al.  A new uncertainty importance measure , 2007, Reliab. Eng. Syst. Saf..

[22]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[23]  Luiz Fernando Oliveira,et al.  Extension of ISA TR84.00.02 PFD equations to KooN architectures , 2010, Reliab. Eng. Syst. Saf..

[24]  Zhenzhou Lu,et al.  Variance-based sensitivity analysis for models with correlated inputs and its state dependent parameter solution , 2017, Structural and Multidisciplinary Optimization.

[25]  Lu Zhenzhou,et al.  Entropy-Based Importance Measure for Uncertain Model Inputs , 2013 .

[26]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[27]  R. Bell,et al.  IEC 61508: functional safety of electrical/electronic/ programme electronic safety-related systems: overview , 1999 .

[28]  Xiang Peng,et al.  Unified uncertainty representation and quantification based on insufficient input data , 2017 .

[29]  Zhenzhou Lu,et al.  Multivariate global sensitivity analysis for dynamic models based on energy distance , 2017, Structural and Multidisciplinary Optimization.

[30]  Sheldon M. Ross,et al.  A First Course in Probability , 1979 .

[31]  Jakob Søndergaard Jensen,et al.  On the consistency of adjoint sensitivity analysis for structural optimization of linear dynamic problems , 2014 .

[32]  E. Lukács.  A Characterization of the Gamma Distribution , 1955 .