Efficient Treatment of Uncertainty in System Reliability Analysis using Importance Measures

Abstract

The reliability of today's electronic products suffers from a growing variability of failure and ageing effects. In this paper, we investigate a technique for the efficient derivation of uncertainty distributions of system reliability. We assume that a system is composed of unreliable components whose reliabilities are modeled as probability distributions. Existing Monte Carlo (MC) simulation-based techniques, which iteratively select a sample from the probability distributions of the components, often suffer from high execution time and/or poor coverage of the sample space. To avoid the costly re-evaluation of a system reliability during MC simulation, we propose to employ the Taylor expansion of the system reliability function. Moreover, we propose a stratified sampling technique which is based on the fact that the contribution (or importance) of the components on the uncertainty of their system may not be equivalent. This technique finely/coarsely stratifies the probability distribution of the components with high/low contribution. The experimental results show that the proposed technique is more efficient and provides more accurate results compared to previously proposed techniques.