Tolerance Analysis Approach based on the Classification of Uncertainty (Aleatory/Epistemic)
Jean‐Yves Dantan, Nicolas Gayton, Ahmed Jawad Qureshi, Maurice Lemaire, Alain Etienne
Abstract
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Jean‐Yves Dantan, Nicolas Gayton, Ahmed Jawad Qureshi, Maurice Lemaire, Alain Etienne
Abstract
Open-access reader
Uncertainty is ubiquitous in tolerance analysis problem. This paper deals with tolerance analysis formulation, more particularly, with the uncertainty which is necessary to take into account into the foundation of this formulation. It presents: a brief view of the uncertainty classification: Aleatory uncertainty comes from the inherent uncertain nature and phenomena and epistemic uncertainty comes from the lack of knowledge a formulation of the tolerance analysis problem based on this classification its development: Aleatory uncertainty is modeled by probability distributions while epistemic uncertainty is modeled by intervals; Monte Carlo simulation is employed for probabilistic analysis while nonlinear optimization is used for interval analysis.
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Uncertainty is ubiquitous in tolerance analysis problem. This paper deals with tolerance analysis formulation, more particularly, with the uncertainty which is necessary to take into account into the foundation of this formulation. It presents: a brief view of the uncertainty classification: Aleatory uncertainty comes from the inherent uncertain nature and phenomena and epistemic uncertainty comes from the lack of knowledge a formulation of the tolerance analysis problem based on this classification its development: Aleatory uncertainty is modeled by probability distributions while epistemic uncertainty is modeled by intervals; Monte Carlo simulation is employed for probabilistic analysis while nonlinear optimization is used for interval analysis.
Key concepts: Uncertainty quantification, Uncertainty analysis, Probabilistic logic, Monte Carlo method, Interval arithmetic, Sensitivity analysis, Propagation of uncertainty, Nonlinear system