Ultrahigh reliability estimates through simulation

Abstract

A statistical variance reduction technique called importance sampling is described, and its effectiveness in estimating ultrahigh reliability of life-critical electronics systems is compared with that of the widely used HARP and SURE analytic tools. Importance sampling is seen to provide more accurate reliability estimates with relatively little computational expense for the models studied. The technique is also seen to provide a convenient method for handling globally time-dependent failure processes and uncertainty in model parameter values. Extreme sensitivity of the importance sample algorithm to its bias parameters is illustrated, and a novel technique for selection of these parameters is proposed.<>