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Probability of Failure of Dynamic Systems by Importance Sampling
ISSN: 1946-3979, e-ISSN: 1946-3987
Published April 08, 2013 by SAE International in United States
Citation: Norouzi, M. and Nikolaidis, E., "Probability of Failure of Dynamic Systems by Importance Sampling," SAE Int. J. Mater. Manf. 6(3):411-415, 2013, https://doi.org/10.4271/2013-01-0607.
Estimation of the probability of failure of mechanical systems under random loads is computationally expensive, especially for very reliable systems with low probabilities of failure. Importance Sampling can be an efficient tool for static problems if a proper sampling distribution is selected. This paper presents a methodology to apply Importance Sampling to dynamic systems in which both the load and response are stochastic processes. The method is applicable to problems for which the input loads are stationary and Gaussian and are represented by power spectral density functions. Shinozuka's method is used to generate random time histories of excitation. The method is demonstrated on a linear quarter car model. This approach is more efficient than standard Monte Carlo simulation by several orders of magnitude.
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