A mixed fuzzy probabilistic approach for risk assessment of dynamic systems

Abstract

Quantifying uncertainty during risk assessment have become important aids for effective decision making and health risk assessment. However, most risk assessment studies suffer from uncertainty analysis, where the issue of uncertainty with respect to model parameter values is of primary importance. Capturing uncertainty in risk assessment is a vital threat for obtain a soundness risk analysis. In this paper, a proposed approach suitable for uncertainty analysis is identified using fuzzy set theory and Monte Carlo simulation. The question then arises as to how these two modes of representation of uncertainty can combined for the purpose of estimating the risk. This approach takes into account both stochastic and subjective uncertainties of information into risk calculation. This study explores areas where random and fuzzy logic models may be applied to improve risk assessment industrial plant involving a dynamic system (change over time).