Uncertainty Modeling in Aeronautical System Testing Campaigns: An approach using Fuzzy Logic and Monte Carlo Simulation

During an aircraft development, several tests are performed to assure the safety of flight and the compliance with certification requirements. These testing campaigns require significant resources, such as prototypes availability, technical staff and material resources. An effective planning is essential to avoid impact on the prototype schedule, and subsequent phases, including marketing and certification campaigns. This work proposes the combination between Fuzzy Theory and Monte Carlo Simulation (MCS) to evaluate the testing campaigns schedule and the risks associated with reducing the planning, through modeling the uncertainties. The Fuzzy Theory handles the subjective data and adverse conditions, while the Monte Carlo Simulation estimates temporal uncertainties, based on probabilistic models and data elicited with specialists.

Keywords: Elicitation, Fuzzy, Fuzzy logic, Fuzzy inference, Monte Carlo simulation, MCS.