System reliability optimisation via quantifying uncertainty
2017-02-24T00:15:44Z (GMT) by
Improving system reliability is becoming an important criterion for engineering industries. In addition to its importance, reliability improvement imposes cost on industries. Therefore, a balance should be found between system reliability and system cost. System reliability optimisation (SRO) models are helpful in reaching this goal. In this study, an approach is proposed to translate system reliability into financial terms. This translation is used to develop mathematical models in order to evaluate and optimise systems. As a result, optimal decisions are found in terms of both reliability improvement and cost reduction. The system structure, for which models are developed, is a multi-state weighted k-out-of-n system with repairable components. Since the reliability of components varies over time, the developed mathematical models simulate the dynamic behaviour of the system over operational periods discretely. Thus, new system reliability evaluation and optimisation models have been presented in this thesis. As another goal, uncertainty associated with system reliability assessment is considered in optimisation models. Linguistic terms of Failure Mode and Effects Analysis (FMEA) are used for system reliability assessment when there is not sufficient data. Based on the qualitative data, an evaluation and optimisation model is proposed to quantify the uncertainty.