Abstract
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.
Highlights
Publisher’s Note: MDPI stays neutralThe use of dynamics and noise behavior as criteria to assess the performance of a rolling bearing are coming into increasing focus besides the lifetime and energy efficiency.In addition to potentially negative health consequences of noise pollution [1], one reason for this is the increasing electrification of passenger cars and the associated sensitivity regarding disturbing and unpleasant noise of all machine elements contained in the technical system [2]
The aim of this paper is to present a procedure for predicting the dynamics of a rolling bearing cage in an angular contact ball bearing using dynamics simulations and regression machine learning algorithms
The application of machine learning regression methods to predict the dynamics of a rolling bearing cage requires data representing the correlation between the varied parameters and the calculated cage dynamics
Summary
Publisher’s Note: MDPI stays neutralThe use of dynamics and noise behavior as criteria to assess the performance of a rolling bearing are coming into increasing focus besides the lifetime and energy efficiency.In addition to potentially negative health consequences of noise pollution [1], one reason for this is the increasing electrification of passenger cars and the associated sensitivity regarding disturbing and unpleasant noise of all machine elements contained in the technical system [2]. Besides unpleasant noise caused by bearing dynamics, in precision applications such as the bearing assembly of the main spindle of machine tools, vibration of the bearing can lead to a negative influence on manufacturing accuracy [3]. Due to the rotation of the rolling element set, the force transmitting points between the inner and outer ring differ. This leads to a changing stiffness and to unavoidable vibrations of the rolling bearing caused by the design itself and is known as variable compliance [4]. The characteristics of these vibrations differ depending on the rolling bearing type (geometry, number of rolling elements, and pitch diameter) and load conditions
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