Multifidelity Data Fusion Mechanism for Digital Twins via the Internet of Things
Hao Wang, Xueguan Song, Chao Zhang · IEEE Internet Computing · 2024
Digital twins (DTs) build the real-time digital mirrors of physical entities and play an important role in various industrial scenarios. The Internet of Things (IoT) serves as the backbone of collecting real-time data for building DTs to meet the technical requirements on real-time responsiveness and modeling precision. We propose a multifidelity data fusion (MDF) mechanism for digital twins via IoT, called MDF-DT. This mechanism establishes the digital twin of a physical entity by fusing real-time sensor data collected via IoT and historical finite-element method simulation data. An improved hierarchical regression for multifidelity data fusion (IHR-MDF) method is proposed to predict high-fidelity (HF) responses based on the low-fidelity samples taken from multiple sources and a small size of HF samples. Numerical experiments show that the normalized root-mean-square error is less than 0.4, and the computational time is about 0.2 ms/point. The proposed MDF-DT mechanism has high applicability in various DT applications.