PSDM: A parametrized structural dynamic modeling method based on digital twin for performance prediction

Digital twin (DT) technology is a powerful tool that accurately represents physical entities and provides real-time monitoring and reliability analysis capabilities for decision-makers and managers. However, modeling complex systems in structural health monitoring using DTs can be repetitive and tedious with existing approaches. To address these issues, we propose a new approach called the Parametrized Structural Dynamic Modeling(PSDM) method. This approach effectively parametrizes dynamic systems in structural health monitoring by leveraging Proper Orthogonal Decomposition (POD) to represent and analyze complex structural behaviors. It also incorporates model reduction and kernel functions. By integrating physics-based insights and advanced modeling methodologies, the PSDM method aims to bridge the gap between the physical and digital domains, enabling accurate and efficient analysis of complex structural systems. To verify its accuracy and efficiency, we apply the PSDM method to two engineering cases: the wing cantilever beam and the telehandler boom. The results demonstrate that the online computational cost of the PSDM method is lower than Finite Element Methods (FEM), thereby improving the computational efficiency of DT modeling technology for complex-large structures. Thus, this research presents a feasible method for implementing structural reliability analysis for engineering equipment.