Abstract
The conventional sequential Monte Carlo Simulation (MCS) considers states in which a component is both in and out of service. Sequential MCS has been applied in different analyses whilst considering both symmetrical and asymmetrical probability distributions. The Beta distribution is however not one of the commonly recommended distributions for use in sequential MCS due to the complexity in deriving its inverse transform. A new sequential MCS technique that applies the Beta distribution is proposed in this paper. The technique is a time-dependent probabilistic approach (TDPA) that uses probability density functions (PDFs) to characterize stochastic network parameters in terms of their season- and time-dependency and simulates the component down (failure) states. The effect of this simulation approach on reliability calculations is analyzed using a published test network. The impact of dispersion and skewness in PDF based input models on a reliability analysis is also investigated. The results show that the TDPA can replicate the conventional sequential MCS analysis. The TDPA computation is also significantly faster. The simulation results of the TDPA also show that dispersion and skewness of component failure rate PDFs significantly influence a reliability analysis.
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