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• W137472514 abstract "A general framework to approach the challenge of uncertaintypropagation in model based prognostics is presented in thiswork. It is shown how the so-called Point Estimate Methods(PEMs) are ideally suited for this purpose because of thefollowing reasons: 1) A credible propagation and representationof Gaussian (normally distributed) uncertainty can bedone with a minimum of computational effort for non-linearapplications. 2) Also non-Gaussian uncertainties can be propagatedby evaluating suitable transfer functions inherently.3) Confidence intervals of simulation results can be derivedwhich do not have to be symmetrically distributed aroundthe mean value by applying PEM in conjunction with theCornish-Fisher expansion. 4) Moreover, the entire probabilityfunction of simulation results can be reconstructed efficientlyby the proposed framework. The joint evaluation of PEMwith the Polynomial Chaos expansion methodology is likelyto provide good approximation results. Thus, non-Gaussianprobability density functions can be derived as well. 5) Thepresented framework of uncertainty propagation is derivativefree,i.e. even non-smooth (non-differentiable) propagationproblems can be tackled in principle. 6) Although the PEMis sample-based the overall method is deterministic. Computationalresults are reproducible which might be importantto safety critical applications. - Consequently, the proposedapproach may play an essential part in contributing to renderthe prognostics and health management into a more credibleprocess. A given study of a generic uncertainty propagationproblem supports this issue illustratively." @default.
• W137472514 created "2016-06-24" @default.
• W137472514 creator A5090202341 @default.
• W137472514 date "2014-07-08" @default.
• W137472514 modified "2023-09-26" @default.
• W137472514 title "A General Framework for Uncertainty Propagation Based on Point Estimate Methods" @default.
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• W137472514 doi "https://doi.org/10.36001/phme.2014.v2i1.1550" @default.
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