Fault Prediction Method of Stage Lifts Based on Digital Twin

To improve the safety of stage lifts’ operation and reduce the failure rate, a digital-twin assisted fault prediction method is proposed here. Signals such as vibration and temperature are collected from the virtual and physical models. A fault prediction model based on Graph Convolutional Networks (GCN) is constructed to identify the potential faults of stage lift. The experiment results illustrate the effectiveness of the proposed model. It would enhance the safety and stability of stage equipment, and provide reliable support for stage performances.