Failure probability estimation of a class of series systems by multidomain Line Sampling

2021 ◽  
Vol 213 ◽  
pp. 107673
Author(s):  
Marcos A. Valdebenito ◽  
Pengfei Wei ◽  
Jingwen Song ◽  
Michael Beer ◽  
Matteo Broggi
Keyword(s):  
Failure Probability ◽  
Probability Estimation ◽  
Line Sampling ◽  
Series Systems

A New Numerical Method for General Failure Probability with Fuzzy Failure Region

2007 ◽  
Vol 353-358 ◽  
pp. 997-1000 ◽  
Author(s):  
Lei Chen ◽  
Zhen Zhou Lu
Keyword(s):  
Numerical Method ◽  
Failure Probability ◽  
High Efficiency ◽  
Joint Probability ◽  
Line Sampling ◽  
Failure Region ◽  
Random Failure ◽  
Total Integral ◽  
Variable Space ◽  
General Reliability

For general reliability analysis with fuzzy failure region F % , the general failure probability F P% is defined as the integral of product of μF% [g( y)] , the membership of performance function ( ) g y to F % , and joint Probability Density Function (PDF) f ( y) over , the total variable space, i.e. [ ( )] ( ) F F P μ g f d % = ∫ ∫ % y y y L . On the basis of line sampling, an efficient method for random failure probability analysis with clear failure region, a new numerical method is presented to calculate F P% . In the presented method, the total integral region is split into m clear sub-regions i F in a way that the value of g( y) in i F can be approximately viewed as i g , a constant independent of y , and the value of [ ( )] F μ % g y in i F can be viewed as a constant ( ) F i μ g % subsequently. Due to the closely invariant property of [ ( )] F μ g % x in i F and 1 2 m = F I F ILI F , F P% is transformed into the sum of ( ) F i μ g % ( ) i F ∫L∫ f y dy , where ( ) i F ∫L∫ f y dy is the random failure probability with the clear failure region i F and can be obtained by line sampling. The high efficiency of the presented method resulted from that of the line sampling is demonstrated by the illustration.

scholarly journals Structural reliability and its sensitivity analysis based on the saddlepoint approximation-line sampling method by dichotomy of golden section

2019 ◽  
Vol 39 (1) ◽  
pp. 11-20
Author(s):  
Ganqing Zhang ◽  
Yanghui Xiang ◽  
Huixin Guo ◽  
Yonghong Nie
Keyword(s):  
Failure Probability ◽  
Structural Reliability ◽  
Golden Section ◽  
Saddlepoint Approximation ◽  
International Study ◽  
Normal Variable ◽  
Line Sampling ◽  
Golden Section Method ◽  
Variable Space ◽  
Solution Efficiency

In order to solve the structural reliability and its sensitivity of the implicit nonlinear performance function (PF) the advantages of the saddlepoint approximation (SA) and line sampling (LS) are merged. Also, the merits of dichotomy and the solution efficiency of the golden section method are combined to propose the saddlepoint approximation-line sampling (SA-LS) method based on the dichotomy of the golden section point. This is complicated and changeable in the non normal variable space, which is a very hot issue of the present international study. For each sample, it is quick to find its zeropoint in PF along the important line sampling direction by the previously mentioned dichotomy so that the structural failure probability can be transformed into the mean of a series linear PFs failure probability, and the reliability sensitivity is just the derivative or partial one of the probability with respect to the relational variables. Examples show that the SA-LS method based on the dichotomy of the golden section point is of high precision and fast velocity in analyzing the structural reliability and sensitivity of the implicit nonlinear PF that are complicated and changeable in the non-normal variable space.

Failure probability estimation of steam turbine blades by enhanced Monte Carlo Method

2015 ◽  
Vol 56 ◽  
pp. 80-88 ◽  
Author(s):  
J.A. Rodríguez ◽  
J.C. Garcia ◽  
E. Alonso ◽  
Y. El Hamzaoui ◽  
J.M. Rodríguez ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Steam Turbine ◽  
Failure Probability ◽  
Turbine Blades ◽  
Probability Estimation ◽  
Steam Turbine Blades

Fuzzy failure probability estimation applying intervening variables

2020 ◽  
Vol 83 ◽  
pp. 101909 ◽  
Author(s):  
Marcos A. Valdebenito ◽  
Michael Beer ◽  
Héctor A. Jensen ◽  
Jianbing Chen ◽  
Pengfei Wei
Keyword(s):  
Failure Probability ◽  
Probability Estimation ◽  
Intervening Variables

scholarly journals Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected Sampling Scheme

2017 ◽  
Vol 17 (2) ◽  
pp. 65-72
Author(s):  
Magdalena Martinásková ◽  
Miroslav Vořechovský
Keyword(s):  
Monte Carlo ◽  
Failure Probability ◽  
Sampling Scheme ◽  
Probability Estimation ◽  
Test Functions ◽  
Random Vectors ◽  
Sampling Schemes ◽  
Alternative Means ◽  
Asymptotic Sampling

Abstract The article examines the use of Asymptotic Sampling (AS) for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions) have been selected in order to test AS with various sampling schemes: (i) Monte Carlo designs; (ii) LHS designs optimized using the Periodic Audze-Eglājs (PAE) criterion; (iii) designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value.

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