Research on importance sampling technique in Monte Carlo method for structural reliability

2015 ◽  
pp. 117-122
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Importance Sampling ◽  
Structural Reliability ◽  
Sampling Technique ◽  
Importance Sampling Technique

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

COMPARISON OF SAMPLING TECHNIQUES FOR A PARAMETRIC YIELD OPTIMIZATION ALGORITHM

1999 ◽  
Vol 06 (02) ◽  
pp. 185-202
Author(s):  
K. PALVANNAN ◽  
YAACOB IBRAHIM
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Simple Algorithm ◽  
Sampling Technique ◽  
Monte Carlo Sampling ◽  
High Yield ◽  
Yield Optimization ◽  
Sampling Density ◽  
Optimal Region ◽  
Importance Sampling Technique

Tolerances in component values will affect a product manufacturing yield. The yield can be maximized by selecting component nominal values judiciously. Several yield optimization routines have been developed. A simple algorithm known as the center of gravity (CoG) method makes use of a simple Monte Carlo sampling to estimate the yield and to generate a search direction for the optimal nominal values. This technique is known to be able to identify the region of high yield in a small number of iterations. The use of the importance sampling technique is investigated. The objective is to reduce the number of samples needed to reach the optimal region. A uniform distribution centered at the mean is studied as the importance sampling density. The results show that a savings of about 40% as compared to Monte Carlo sampling can be achieved using importance sampling when the starting yield is low. The importance sampling density also helped the search process to identify the high yield region quickly and the region identified is generally better than that of Monte Carlo sampling.

An Importance Sampling Approach for Time-Dependent Reliability

2011 ◽  
Author(s):  
Amandeep Singh ◽  
Zissimos P. Mourelatos ◽  
Efstratios Nikolaidis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Importance Sampling ◽  
Sampling Technique ◽  
Process Time ◽  
Cumulative Probability ◽  
Repairable Systems ◽  
Importance Sampling Method ◽  
True Value

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. The reliability usually degrades with time increasing the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time. In this article, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available which provide an upper bound to the true reliability which may overestimate the true value considerably. The traditional Monte-Carlo simulation is accurate but computationally expensive. A computationally efficient importance sampling technique is presented to calculate the cumulative probability of failure for random dynamic systems excited by a stationary input random process. Time series modeling is used to characterize the input random process. A detailed example demonstrates the accuracy and efficiency of the proposed importance sampling method over the traditional Monte Carlo simulation.

Structural Reliability Analysis With Cross Entropy and Low Discrepancy Sampling Methods

2011 ◽  
Author(s):  
K. Pugazhendhi ◽  
A. K. Dhingra
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Structural Reliability ◽  
Sampling Methods ◽  
Reliability Evaluation ◽  
Cross Entropy ◽  
Sampling Density ◽  
Quasi Monte Carlo ◽  
Importance Sampling Techniques ◽  
Optimum Sampling

In recent years quasi Monte-Carlo (QMC) techniques are gaining more popularity for reliability evaluation because of their increased accuracy over traditional Monte-Carlo simulation. A QMC technique like Low Discrepancy Sequence (LDS) combined with importance sampling is shown to be more accurate and robust in the past for the evaluation of structural reliability. However, one of the challenges in using importance sampling techniques to evaluate the structural reliability is to identify the optimum sampling density. In this article, a novel technique based on a combination of cross entropy and low discrepancy sampling methods is used for the evaluation of structural reliability. The proposed technique does not require an apriori knowledge of Most Probable Point of failure (MPP), and succeeds in adaptively identifying the optimum sampling density for the structural reliability evaluation. Several benchmark examples verify that the proposed method is as accurate as the quasi Monte-Carlo technique using low discrepancy sequence with the added advantage of being able to accomplish this without a knowledge of the MPP.

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