Adaptive Monte Carlo Simulation Method and Its Applications to Reliability Analysis of Series Systems with a Large Number of Components

Xin Liu ◽  
Zi-Jun Cao ◽  
Dian-Qing Li ◽  
Yu Wang
2007 ◽  
Vol 353-358 ◽  
pp. 66-69
Bing Yang ◽  
Yong Xiang Zhao ◽  
Wei Hua Zhang

Because of the huge cost involved in data acquisition, probabilistic S-N relations should be given on a wide applicable sense to perform the reliability analysis at arbitrary survival probability-confidence (P-C) level. However, some existent fatigue databases give the material probabilistic S-N relations only with several fixed P-C levels. To realize the reliability analysis on a sense of arbitrary P-C level, a Monte Carlo simulation method is presented for reconstruction of the relations. Test data are re-gotten by a method under the new simulation policy that matching production practice according to original statistical parameters. Details are given with respect to the possible cases of known conditions. The relations are finally determined by maximum likelihood estimation on a general model to realize the analysis at arbitrary P-C level. Reconstruction of the relations for 60Si2Mn spring steel has indicated the availability and feasibility of present method.

2012 ◽  
Vol 215-216 ◽  
pp. 754-757 ◽  
Jun Zhang ◽  
Bing Zhang ◽  
Rong Gang Yu

A simulation-based reliability analysis approach for kinematics accuracy of retracting mechanism is presented. The parametric variable model with linkage dimension error and joint clearance of retracting mechanism is modeled in ADAMS, adopting the Monte Carlo simulation method to analysis the influence of kinematic accuracy. The flowchart of the approach has been presented. Finally, the retracting mechanism is taken as an example to validate the proposed method. The results show that it is more accurate than the traditional methods.

Structures ◽  
2018 ◽  
Vol 14 ◽  
pp. 209-219 ◽  
M. Gordini ◽  
M.R. Habibi ◽  
M.H. Tavana ◽  
M. TahamouliRoudsari ◽  
M. Amiri

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

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