A binary decision diagram method for phased mission analysis of non-repairable systems

Phased mission analysis is carried out to predict the reliability of systems which undergo a series of phases, each with differing requirements for success, with the mission objective being achieved only on the successful completion of all phases. Many systems from a range of industries experience such missions. The methods used for phased mission analysis are dependent upon the repairability of the system during the phases. If the system is non-repairable, fault-tree-based methods offer an efficient solution. For repairable systems, Markov approaches can be used. This paper is concerned with the analysis of non-repairable systems. When the phased mission failure causes are represented using fault trees, it is shown that the binary decision diagram (BDD) method of analysis offers advantages in the solution process. A new way in which BDD models can be efficiently developed for phased mission analysis is proposed. The paper presents a methodology by which the phased mission models can be developed and analysed to produce the phase failure modes and the phase failure likelihoods

Reliability and Cost Impacts for Attritable Systems

Attritable systems trade system attributes like reliability and reparability to achieve lower acquisition cost and decrease cost risk. Ultimately, it is hoped that by trading these attributes the amount of systems able to be acquired will be increased. However, the effect of trading these attributes on system-level reliability and cost risk is difficult to express complicated reparable systems like an air vehicle. Failure-time and cost data from a baseline limited-life air vehicle is analyzed for this reliability and reparability trade study. The appropriateness of various reliability and cost estimation techniques are examined for these data. This research employs the cumulative incidence function as an input to discrete time non-homogeneous Markov chain models to overcome the hurdles of representing the failure-time data of a reparable system with competing failure modes that vary with time. This research quantifies the probability of system survival to a given sortie, S(n), average unit flyaway cost (AUFC), and cost risk metrics to convey the value of reliability and reparability trades. Investigation of the benefit of trading system reparability shows a marked increase in cost risk. Yet, trades in subsystem reliability calculate the required decrease in subsystem cost required to make such a trade advantageous. This research results in a trade-space analysis tool that can be used to guide the development of future attritable air vehicles