Adaptive optimal sampling methodology for zone-based probabilistic life prediction

Simulation-based system reliability prediction may require significant computations, particularly when the expected value of the system failure probability is relatively low. In this paper, a methodology is presented for variance reduction of sampling-based series system reliability predictions based on optimal allocation of Monte Carlo samples to the individual failure modes. An algorithm is presented for adaptively allocating samples to member failure modes based on initial estimates of the member failure probabilities pi. The methodology is demonstrated for a simple series system and a gas turbine engine disk modeled using a zone-based series system approach. For the example considered, it is shown that the computational accuracy of the method does not appear to depend on the initial pi estimate. However, the computational efficiency is highly dependent on the initial pi estimate. The results can be applied to improve the efficiency of sampling-based series system reliability predictions. Nomenclature COVi = coefficient of variation of the failure probability for member i E (•) = mean valu