Advanced Reliability Analysis Method For Mechanisms Based On Uncertain Measure

In traditional mechanism reliability analysis, probability theory or statistical approaches are employed. However, these methods cannot be used under lack of data and great epistemic uncertainty. In this paper, an advanced mechanism reliability analysis method is put forward based on uncertain measure. To satisfy the subadditivity of epistemic uncertainties, a novel uncertainty quantification method based on uncertainty theory is proposed for mechanism reliability analysis. Then, a point kinematic reliability analysis method combined with uncertain measure is presented to calculate the kinematic uncertainty reliability of motion mechanism at each time instant. Three models are developed for estimating kinematic uncertainty reliability. Furthermore, first-order Taylor series expansion is used to solve nonlinear limit state functions. A new kinematic uncertainty reliability index (KURI) is presented based on normal uncertainty distribution. Finally, by applying the proposed method to a numerical experiment, the trend of uncertainty reliability was found to be consistent with the traditional method. The two practical engineering applications show that the presented method are more reasonable compared with the classical approaches when the information of design parameters is insufficient.