Optimal sensor placement for digital twin based on mutual information and correlation with multi-fidelity data

This paper proposes an optimal sensor placement algorithm based on mutual information and correlation, which is particularly suitable for solving sensor placement problems in digital twins. Digital twins roughly comprise two data types: (1) the sensor data collected from a physical entity and (2) the simulation data collected from the physics-based simulation models. The proposed method takes advantage of the two data types to select sensor locations. Specifically, it determines sensor locations by maximizing the mutual information between the selected and uninstrumented candidate locations. In addition, the correlation between the sensor data and simulation data is characterized by a covariance matrix and used in the determination of mutual information. Moreover, the proposed method is also applicable in multiworking conditions by using clustering algorithms. To verify the effectiveness and applicability of the proposed sensor placement method, it was compared with two other sensor placement methods in terms of reconstruction accuracy through a digital twin case of a crane boom. The results show that the proposed method exhibits excellent performance.