Availability with input uncertainties using an interval-based approach

In availability predictive analysis not every model input parameter is known exactly when the model is developed. The sources of uncertainties include lack of data and lack of knowledge about physical systems. In this paper, we present a reliable mechanism to help improve evaluation robustness when significant uncertainties exist. The mechanism incorporates variabilities and uncertainties based on imprecise distribution parameters in the availability evaluations are intervals instead of precise real numbers. The analysis takes into account the effects of variabilities in MTTR and MTTF when calculating interval availabilities. Interval studies of availability models should be used to show the influence of input parameters uncertainties on resultant availability evaluation. Interval methods and interval arithmetics have been applied in Scilab toolbox Int4Sci framework for steady state solution of availability models.