Graph Neural Networks for Dynamic Modeling of Roller Bearing
Vinay Sharma Jens Ravesloot Cees Taal Olga Fink
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.