Dynamically adaptive cascading updates for hierarchical digital twins

Traditional sensors encounter challenges such as high collection costs, insufficient measurement points, and low data quality in the monitoring and maintenance of modern equipment. These challenges significantly affect the effectiveness and efficiency of monitoring and maintenance processes. Digital twin (DT) technology, as a digital replica of physical entities, is regarded as the 'digital sensor' of physical entities due to its high-precision modeling and dynamic updating capabilities. Compared to traditional sensors, DT models provide substantial improvements in both data volume and quality. However, creating a DT model with high precision and robust dynamic characteristics is notably challenging, particularly when the relationships and state features of the physical entity are complex and variable. To address this issue, a cascading update strategy was introduced. This strategy coordinates complex hierarchical DT update tasks, ensuring model accuracy. Furthermore, a signal characteristic-based dynamic adaptive update algorithm is proposed. This algorithm optimizes the DT updating process and enhances the model's dynamic characteristics. The proposed method is validated using experimental data on plunger pump barrel-port plate oil leakage. The results demonstrate that the method significantly improves the accuracy and updating efficiency of the DT model. It achieves a balance between precision and update time costs, enhancing DTs accuracy and practicality as a 'digital sensor'.