Nonlinear Identification of a Gear Transmission System Using Numerical and Experimental Methods

Abstract eng:
Nonlinear 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.

• Export as: BibTeX | MARC | MARCXML | DC | EndNote | NLM | RefWorks
• Add to your basket