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The dynamic behavior of a cage can have a significant influence on the performance and the noise of a rolling bearing. In case of an unstable or high-frequency cage movement, vibrations are transmitted to the adjacent machine elements and the environment, which can influence the operating behavior of the whole system. In addition, a loud noise can often be perceived, which is referred to in the literature as cage rattling or squealing. In this paper, characteristics of different cage movements are investigated using multibody simulations and experimental investigations. For this purpose, essential properties of the fundamentally observable cage movement types “stable”, “unstable” and “circling” are presented. The calculated cage dynamics and the type of cage motion are used to show dependencies between the operating conditions and the resulting cage movement such as inner ring rotational speed, bearing load or cage characteristics such as pocket clearance. Based on the simulations, interactions between the input parameters can also be determined. The results are used to identify operation-critical conditions and cage properties that lead to high cage dynamics. Finally, a comparison between the results of the multibody calculations and optical measurements is made. The optical measurements are performed using high-speed cameras. Reference markers fixed on the cage and digital image correlation allow the evaluation of the kinematics as well as the deformation of the cage. These results are compared with the simulation data to ensure a high quality of dynamics simulation.

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Rolling bearings have to meet the highest requirements in terms of guidance accuracy, energy efficiency, and dynamics. An important factor influencing these performance criteria is the cage, which has different effects on the bearing dynamics depending on the cage’s geometry and bearing load. Dynamics simulations can be used to calculate cage dynamics, which exhibit high agreement with the real cage motion, but are time-consuming and complex. In this paper, machine learning algorithms were used for the first time to predict physical cage related performance criteria in an angular contact ball bearing. The time-efficient prediction of the machine learning algorithms enables an estimation of the dynamic behavior of a cage for a given load condition of the bearing within a short time. To create a database for machine learning, a simulation study consisting of 2000 calculations was performed to calculate the dynamics of different cages in a ball bearing for several load conditions. Performance criteria for assessing the cage dynamics and frictional behavior of the bearing were derived from the calculation results. These performance criteria were predicted by machine learning algorithms considering bearing load and cage geometry. The predictions for a total of 10 target variables reached a coefficient of determination of R2≈0.94 for the randomly selected test data sets, demonstrating high accuracy of the models.