Data Super-Network Fault Prediction Model and Maintenance Strategy for Mechanical Product Based on Digital Twin

When mechanical products work in complex environments, it is imperative to build an optimal maintenance strategy, based on accurate positioning of fault locations and prediction of fault conditions. Based on digital twinning technology, this paper proposes a “super-network-warning features” fault prediction and maintenance method. According to the digital twin five-dimensional structure, a three-layer super-network model is constructed, providing a quantitative research for data among heterogeneous subjects in digital twinning. Early-warning-features in the physical layer, virtual layer and service layer are selected as input parameters of the fault prediction model to accurately predict the cause of the fault. Then, using the simulation and optimization functions of the virtual model in digital twinning, a real-time maintenance strategy is formulated for the causes of the fault. It supplements the missing link between fault prediction and maintenance. Taking an aero-engine bearing as an example, this method is compared with a traditional method. The results show that the model prediction error of this method is better than the traditional method.