Maintaining high levels of availability and reliability are essential objectives for many industries, especially those that are subject to high costs due to non-payment as a result of shutdowns of critical systems, e.g. gas turbines. To utilize these systems as effectively as possible, maintenance must be optimized. Though, determining what is optimal is a tough multi-variable task requiring detailed knowledge about components building the system, such as damage mechanisms, TMF/LCF crack initiation and propagation, creep deformation, creep damage, general material deterioration, erosion, oxidation and corrosion. Also, customer specific inputs are essential, e.g. value of the produced entity, fuel prices, unplanned and planned standstill cost. To efficiently funnel this information into a customer adapted, optimized maintenance plan, a probabilistic optimization model is proposed. The model will dynamically calculate the most efficient point in time for a renewal. Further, the model can, as a function of risk willingness, adjust the maintenance plan to any customer’s specific demands. This paper describes (i) the model, (ii) how information is gathered and processed, (iii) how the risk assessment is performed and (iv) how the lifetime prediction is carried out. The optimization itself can be adjusted to aim at: minimizing cost or risk or maximizing availability, performance or reliability. Also, this paper describes how the quantitative selection of critical components included in the optimization is performed.
Selection of highly efficient small interference RNA (SiRNA) targeting mammalian genes