Modern industrial systems assume different configurations to accomplish multiple objectives during different phases of operation, and the component parameters may also vary from one phase to the next. Consequently, reliability evaluation of complex multi-phased systems is a vital and challenging issue. Maximization of mission reliability of a multi-phase system via optimal asset selection is another key demand; incorporation of optimization issues adds to the complexities of reliability evaluation processes. Introduction of components having self-diagnostics and self-recovery capabilities, along with increased complexity and phase-dependent configuration variations in network architectures, requires new approaches for reliability evaluation.
This paper considers the problem of evaluating the reliability of a complex multi-phased system with self-recovery/fault-protection options. The reliability analysis is based on a colored digraph (i.e., multi-functional) model that subsumes fault trees and digraphs as special cases. These models enable system designers to decide on system architecture modifications and to determine the optimum levels of redundancy. A sum of disjoint products (SDP) approach is employed to compute system reliability. We also formulated the problem of optimal asset selection in a multi-phase system as one of maximizing the probability of mission success under random load profiles on components. Different methods (e.g., ordinal optimization, robust design, and nonparametric statistical testing) are explored to solve the problem. The resulting analytical expressions and the software tool are demonstrated on a generic programmable software-controlled switchgear, a data bus controller system and a multi-phase mission involving helicopters.