Abstract

Cage instability or highly dynamic cage movement can have a strong influence on the performance of rolling bearings. In addition to very loud and disturbing noises (“squeal”), bearing failure due to cage fracture can occur.

This article deals with two topics: the general classification of cage motions and the prediction of application-dependent cage motions to prevent cage instability during operation. The dependencies of the unstable cage movement on the bearing’s load and geometric characteristics of the cage are analyzed using a large number of sophisticated simulations, based on multibody dynamics. To evaluate the cage movements, first a key figure called the “cage dynamics indicator” (CDI) is introduced, which is used to classify the simulation results by means of quadratic discriminant analysis into three types “unstable,” “stable,” and “circling” (= classification of cage motion). Second, a machine learning algorithm trained and tested on the basis of more than 4,000 simulation results enables a time-efficient prediction of the physical correlations between bearing load and cage properties and the resulting cage dynamics (= prediction of cage motion). A comparison of the calculated cage dynamics with the results of an optical measurement of the cage dynamics rounds off this article. This comparison illustrates the high quality of the simulation models and the training data used for machine learning.

摘要

保持架不稳定性或高动态保持架运动会对滚动轴承的性能产生很大影响。除了非常响亮和令人不安的噪音（“尖叫声”）外，还可能发生因保持架断裂而导致的轴承故障。

本文涉及两个主题：笼子运动的一般分类和依赖于应用程序的笼子运动的预测，以防止笼子在运行过程中不稳定。基于多体动力学，使用大量复杂的模拟分析了不稳定的保持架运动对轴承载荷和保持架几何特性的依赖关系。为了评估笼子的运动，首先引入了一个称为“笼子动力学指标”（CDI）的关键数字，用于通过二次判别分析将模拟结果分为“不稳定”、“稳定”和“不稳定”三种类型。盘旋”（=笼子运动的分类）。二、在4个以上的基础上训练和测试过的机器学习算法，000 模拟结果能够高效地预测轴承载荷和保持架特性之间的物理相关性以及由此产生的保持架动力学（= 保持架运动的预测）。将计算出的笼子动力学与笼子动力学的光学测量结果进行比较，使本文更加完善。此比较说明了用于机器学习的仿真模型和训练数据的高质量。