Use of high-fidelity fatigue models that incorporate not only material uncertainty but also part variability and operational uncertainties can improve the accuracy of predictive maintenance and thus decrease operational cost. However, very little work has been done to explore this field due to the number of computationally heavy cost function evaluations needed for this. Here a general statistical framework that applies Maximum Entropy Method (MEM), fractional statistical moments and Multiplicative Dimensional Reduction (M-DRM) to estimate expected life probability distributions with low computational cost is tested on advanced models of a 6204 SKF ball bearing. The influence of critical part tolerances and load conditions on fatigue life in a FEA and a non-linear analytical model are quantified with a probability density function with 80 function evaluations, which is one order of magnitude lower than necessary for a comparable accuracy achieved by Monte Carlo simulation.


Uncertainty Quantification With Maximum Entropy Method for Fatigue Life Estimation

Division / Sponsors: 
Design Engineering Division and Computers & Information In Engineering Division


Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2020/volume/83907

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