Article2017

A New Reliability Analysis Method Combining Adaptive Kriging With Weight Index Monte Carlo Simulation

Jiangang Ma · Ziyan Ren · Guoxin Zhao · Yanli Zhang · Chang Seop Koh

17 citations0/0 files storedIEEE Transactions on Magnetics

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Probabilistic and Robust Engineering Design Reliability and Maintenance Optimization Advanced Multi-Objective Optimization Algorithms

Abstract

In order to extend the application of reliability-based design optimization to engineering electromagnetic problems, the main contribution of this paper is to propose an efficient reliability analysis method: adaptive Kriging-assisted weight index Monte Carlo Simulation (MCS). The Kriging model is constructed to substitute for expensive finite-element analysis of engineering problem during optimization and reliability analysis. To improve the interpolation accuracy of Kriging, a learning function is adopted in adaptive sampling process. A weight index form of MCS method is proposed to enhance the efficiency of conventional MCS method. Finally, taking the result of conventional MCS as a reference, proposed reliability analysis method is compared with reliability index approach and sensitivity-assisted MCS methods.

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References

2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation 2012A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models 2009Benchmark study of numerical methods for reliability-based design optimization 2016LIF: A new Kriging based learning function and its application to structural reliability analysis 2013AK-SYS: An adaptation of the AK-MCS method for system reliability 2012A new efficient simulation method to approximate the probability of failure and most probable point 2008Reliability analysis of structures using artificial neural network based genetic algorithms 2015A local Kriging approximation method using MPP for reliability-based design optimization

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