000015622 001__ 15622
000015622 005__ 20161115135325.0
000015622 04107 $$aeng
000015622 046__ $$k2013-06-12
000015622 100__ $$aGiagopoulos, D.
000015622 24500 $$aNonlinear Identification of a Gear Transmission System Using Numerical and Experimental Methods

000015622 24630 $$n34.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000015622 260__ $$bNational Technical University of Athens, 2013
000015622 506__ $$arestricted
000015622 520__ $$2eng$$aNonlinear modelling and parametric identification of a gear-pair system supported on bearings with rolling elements, are employed in the present work. First, a nonlinear mathematical model is introduced. In this model, the housing of the gearbox is modeled by using finite elements, while the essential effects of the gear-pair, the bearings and the shafts are taken into account via a lumped nonlinear mathematical model. This model possesses strongly nonlinear characteristics, accounting for gear backlash and bearing stiffness nonlinearities. Then, a Bayesian uncertainty quantification and propagation (UQ&P) framework is adopted in order to estimate the optimal values of the gearbox, gear-pair and bearing model parameters. In order to identify the values of the parameters, accelerations time histories are used, obtained during various operating conditions of the gearbox. These measurements are recorded from a special experimental device, which was designed and set up for this purpose. The effect of correlation in the prediction error models postulated in the Bayesian model selection and parameter estimation technique is investigated. Computationally intensive stochastic simulation algorithms (e.g., Transitional MCMC) are suitable tools for identifying system and uncertainty models as well as for performing robust prediction analyses. These algorithms require a quite large number of system analyses to be performed over the space of uncertain parameters, which leads frequently to excessive computational time. Efficient computing techniques are integrated with the Bayesian framework to handle large order models and localized nonlinear action. In particular, component mode synthesis and automated multilevel substructuring techniques are proposed to achieve substantial reductions in computational effort.

000015622 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000015622 653__ $$a

000015622 7112_ $$aCOMPDYN 2013 - 4th International Thematic Conference$$cIsland of Kos (GR)$$d2013-06-12 / 2013-06-14$$gCOMPDYN2013
000015622 720__ $$aGiagopoulos, D.$$iPapadioti, D.$$iPapadimitriou, C.$$iNatsiavas, S.
000015622 8560_ $$ffischerc@itam.cas.cz
000015622 8564_ $$s313960$$uhttps://invenio.itam.cas.cz/record/15622/files/1164.pdf$$yOriginal version of the author's contribution as presented on CD, section: CD-MS 19 IDENTIFICATION METHODS IN STRUCTURAL DYNAMICS
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000015622 962__ $$r15525
000015622 980__ $$aPAPER