Reliability Evaluation of Phased-Mission Systems Using Stochastic Computation

A phased-mission system (PMS) usually consists of several nonoverlapping phases of tasks. All phases are required to be accomplished sequentially for a successful mission. Different features must be considered in the reliability evaluation of a PMS, including the dependence among the phases with respect to a common component and the different system topologies for the phases. To overcome the limitation of existing approaches, a stochastic computational approach is proposed for efficiently analyzing the reliability of a nonrepairable PMS. Stochastic logic models are proposed to analyze the common components in the different phases. In the stochastic analysis, the signal probabilities of the basic components are encoded as non-Bernoulli sequences of random permutations with fixed numbers of 1s and 0s. Thus, the proposed stochastic approach can be used to evaluate a PMS under any distribution. Based on the generated stochastic sequences for the basic components and the system topology, the failure probability of the PMS can be efficiently predicted. Several case studies are evaluated to show the accuracy and efficiency of the stochastic approach. Compared with a combinatorial analysis, the accuracy of the stochastic analysis varies with the length of the stochastic sequences. However, it is shown that the stochastic analysis is more efficient than a Monte Carlo simulation at the same execution complexity in the number of runs.