In the presented work, we propose to apply the framework of graph neural networks (GNNs) to predict the dynamics of a rolling element bearing. This approach offers generalizability and interpretability, having the potential for scalable use in real-time operational digital twin systems for monitoring the health state of rotating machines. By representing the bearing's components as nodes in a graph, the GNN can effectively model the complex relationships and interactions among them. We utilize a dynamic spring-mass-damper model of a bearing to generate the training data for the GNN. In this model, discrete masses represent bearing components such as rolling elements, inner raceways, and outer raceways, while a Hertzian contact model is employed to calculate the forces between these components. We evaluate the learning and generalization capabilities of the proposed GNN framework by testing different bearing configurations that deviate from the training configurations. Through this approach, we demonstrate the effectiveness of the GNN-based method in accurately predicting the dynamics of rolling element bearings, highlighting its potential for real-time health monitoring of rotating machinery.

翻译：在本研究中，我们提出应用图神经网络框架预测滚动轴承的动态行为。该方法兼具泛化能力与可解释性，具备在旋转机械健康状态实时监测的数字孪生系统中规模化应用的潜力。通过将轴承组件表示为图中的节点，图神经网络能够有效建模各组件间的复杂关系与相互作用。我们采用轴承的动态弹簧-质量-阻尼模型生成图神经网络的训练数据：该模型以离散质量表征滚动体、内圈滚道与外圈滚道等轴承组件，并通过赫兹接触模型计算组件间的相互作用力。通过测试与训练配置不同的轴承结构，我们评估了所提图神经网络框架的学习与泛化能力。结果表明，基于图神经网络的方法能够准确预测滚动轴承动态特性，凸显了其在旋转机械实时健康监测中的应用潜力。