Optimal Sensor Placement for Developing Reliable Digital Twins of Structures

Sensor networks are mounted on structures to collect information for addressing a number of important but competing tasks involved in building a reliable digital twin from the collected data. These monitoring tasks include (1) modal identification under low vibration measurements assuming that the system can behave linearly, (2) physics-based model selection and model parameter estimation under various vibration levels activating nonlinear mechanisms at subsystem levels, (3) virtual sensing and response reconstruction over the whole body of the structure using the information from the limited number of sensors, and finally (4) structural health monitoring and damage identification (location and severity). Optimal sensor configuration (OSC) designs (type, number and location of sensors) have been developed in the past to address individual tasks, making assumptions about the loads, models and environmental conditions. However, the sensor network should be designed to collect data that are informative for all tasks simultaneously. In addition, the OSC design should be made robust to modelling, loading and environmental uncertainties. Cost issues related to budget availability for implementing and maintaining a sensor configuration should also be considered in the sensor network design. In this work, a multi-objective OSC framework based on utility functions that are built from information theoretic measures and cost considerations is presented for accounting simultaneously for the aforementioned tasks and thus using cost-effective information extracted from the physical sensing system for developing reliable digital twins. The Kullback-Liebler divergence is used to quantify the information gain from a sensor network, and heuristic algorithms to solve the multi-objective optimization problem are proposed.