Research on Bearing Digital Twin Modeling and Residual Life Predictive Simulation Based on Deep Learning

Abstract

Modern Digital Twin models are normally built based on massive live data from the manufacturing life-cycle to realize the real-time virtual representation of the physical system and applied in predictive simulation for optimization suggestions and fault warnings for the subsequent operation. Four residual life prediction methods based on deep learning are established to build Bearing Digital Twin models, including the classical CNN and RNN, as well as the LSTM and CNN-LSTM. The real rolling bearing digital twin models are setup and predictive simulated based on the given datasets and evaluation indicators. The simulation results are analyzed and the best performance models for Bearing residual life prediction are stated as a conclusion. Some future research points on deep learning based digital twin modeling and simulation are proposed.