Importance sampling for stochastic systems under stationary noise having a specified power spectrum

We develop an importance sampling simulation scheme for estimating an extremely small probability of system failure with respect to a time-dependent stochastic system excited by stationary random noise having a specified power spectrum. First, we construct a system of random differential equations driven by a Wiener process, which can approximately give such a stationary random noise by the use of an extended version of the well-known Ornstein–Uhlenbeck process. Next, we suppose a stochastic response system driven by the constructed stationary random noise. Next, formulating the probability of system failure, we give an importance sampling scheme through the probability measure transformation based upon the Girsanov theorem, where multi design times are introduced to cope with stationary or almost stationary behavior of the system. Finally, we give numerical examples to demonstrate the efficiency of the proposed scheme.