An advanced method for the sensitivity analysis of safety system
- 18 Downloads
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.
KeywordsSafety system Sensitivity analysis Single-loop Monte Carlo Analytical deduction Safety integrity level
This work is supported by the Natural Science for Youth Foundation of China (Grant 71701210) and the Aeronautical Science Foundation of China (Grant 20165196017).
- Gupta AK, Zeng WB, Wu Y (2010) Exponential distribution. Reliab Eng Syst Safe 91(6):689–697Google Scholar
- IEC (1998) Functional safety of electrical/electronic/programmable electronic safety-related systems. IEC 61508, Parts 1–7, 1st edn. GenevaGoogle Scholar
- Peng X, Li J, Jiang S (2017) Unified uncertainty representation and quantification based on insufficient input data. Struct Multidiscip Optim 7:1–13Google Scholar
- Rouvroye JL (2001) Enhanced markov analysis as a method to assess safety in the process. Technische Universiteitndhoven, EindhovenGoogle Scholar
- Tang ZC, Lu ZZ, Wang P (2012) An entropy-based global sensitivity analysis for the structures with both fuzzy variables and random variables. Mech Eng Sci 227(2):195–212Google Scholar
- Xiao SN, Lu ZZ, Wang P (2018) Multivariate global sensitivity analysis for dynamic models based on energy distance. Struct Multidiscip Optim 57(1):279–291Google Scholar
- Yun WY, Lu ZZ, Jiang X (2017) A modified importance sampling method for structural reliability and its global reliability sensitivity analysis. Struct Multidiscip Optim 6:1–17Google Scholar
- Zong WG (2005) Optimal cost design of water distribution networks using harmony search. Eng Optim 8(3):259–277Google Scholar