This study investigates issues related to parametric identification of a typical power train component with nonlinear characteristics. In particular, a gear-pair system supported on bearings with rolling elements is selected. This model accounts for gear backlash and bearing stiffness nonlinearities. A Bayesian statistical framework is first adopted in order to estimate the optimal values of the gear and bearing model parameters. This is achieved by combining experimental information from vibration measurements with theoretical information built into a parametric mathematical model of the system. Then, characteristic numerical results are presented. The emphasis is put on explaining some of the peculiar results obtained by applying classical gradient-based optimisation methodologies for the strongly nonlinear system examined. Some serious difficulties, associated with the existence of irregular response or the coexistence of multiple motions, are first pointed out. A solution to some of these problems, through the application of a suitable genetic algorithm, is then presented.

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