Influence of aging steel on pipeline burst pressure prediction and its impact on failure probability estimation

2021 ◽  
Vol 120 ◽  
pp. 104950
Author(s):  
N.E. González-Arévalo ◽  
J.C. Velázquez ◽  
M. Díaz-Cruz ◽  
A. Cervantes-Tobón ◽  
G. Terán ◽  
...  
Keyword(s):  
Failure Probability ◽  
Probability Estimation ◽  
Burst Pressure ◽  
Aging Steel

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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