Nonintrusive-polynomial-chaos-based kinematic reliability analysis for mechanisms with mixed uncertainty

Due to the scarcity of statistical data, epistemic uncertainties are inevitable in the mechanism. As a promising uncertainty quantification technique, polynomial chaos has advantages over other methods in terms of accuracy and efficiency. In this paper, an improved nonintrusive polynomial chaos method is proposed for the kinematic reliability analysis of the mechanism with fuzzy and random variables as well as fuzzy failure/safety states. Klir log-scale transformation is applied to unify the fuzzy and random variables. Meanwhile, the polynomial-chaos-based probability formula of the fuzzy event is developed to characterize the fuzzy failure/safety states. The proposed method is applied to the reliability analysis of a retractable mechanical system. The results show good accuracy and efficiency of the proposed method when compared with the response surface method (RSM), Kriging method, and Monte Carlo simulation (MCS).