Uncertainty Analysis of the Remote Exploration and Experimentation System

Abstract

DOI: 10.2514/1.51380 This paper discusses a method for computing the uncertainty in output metrics of stochastic models due to epistemic uncertainties in the model-input parameters. The method is illustrated by applying it to compute the distribution and confidence interval of the reliability of the Remote Exploration and Experimentation system. This method makes use of Monte Carlo sampling and propagates the epistemic uncertainty in the model parameters through the system reliability model. It acts as a wrapper to already-existing models as well as their solution tools/ techniquesandhasawiderangeofapplicability.Althoughitisasampling-basedmethod,nosimulationiscarriedout whenperforminguncertaintypropagationthroughanalyticmodels,butanalyticoranalytic–numericsolutionofthe underlying stochastic model is performed for each set of input-parameter values, sampled from their distributions. Using the input epistemic uncertainty, in the form of 95% confidence intervals of input parameters of the stochastic model, the two-sided 95% confidence interval of reliability of the Remote Exploration and Experimentation system at a time t 5 years is computed to be (0.949485, 0.981994).