Rotating unbalance is one of the most important critical parameters that causes operational failures of rotating machinery. The uneven distribution of mass on the structure of the rotors creates heavy spots, which must be eliminated to avoid generating excessive stress on the rotor bearings. The main objective of this work is to perform the uncertainty analysis on rotating machine systems supported with rolling bearings. A computational procedure is implemented to achieve this objective that can qualitatively represent the main behaviors and parameters of rotating machines. Further, methods of uncertainty quantification are applied to verify the behavior of the system given the probability density functions of the input parameters. One of the most commonly used methods is the Monte Carlo method, which requires thousands of simulations to produce accurate results. This method is used to obtain means and standard deviations of the system responses, given the means and standard deviations of the inputs. In our work, Monte Carlo simulation has been successfully used as a reference stochastic solver to evaluating the variability of the dynamic responses.

Rotating unbalance is one of the most important critical parameters that causes operational failures of rotating machinery. The uneven distribution of mass on the structure of the rotors creates heavy spots, which must be eliminated to avoid generating excessive stress on the rotor bearings. The main objective of this work is to perform the uncertainty analysis on rotating machine systems supported with rolling bearings. A computational procedure is implemented to achieve this objective that can qualitatively represent the main behaviors and parameters of rotating machines. Further, methods of uncertainty quantification are applied to verify the behavior of the system given the probability density functions of the input parameters. One of the most commonly used methods is the Monte Carlo method, which requires thousands of simulations to produce accurate results. This method is used to obtain means and standard deviations of the system responses, given the means and standard deviations of the inputs. In our work, Monte Carlo simulation has been successfully used as a reference stochastic solver to evaluating the variability of the dynamic responses.