Dynamic Sensor Selection for Efficient Monitoring of Coupled Multidisciplinary Systems

Abstract

Coupled multidisciplinary systems involve different disciplines/subsystems with feedback-coupled interactions, illustrating the complex interdependencies inherent in real-world engineering systems. Effective monitoring of a coupled multidisciplinary system is crucial for real-time assessment of the interactions between various disciplines within the system. This monitoring provides the data necessary for detecting and addressing issues in a timely manner and facilitates adaptive decision-making for taking reliable design or control actions. However, processing and analyzing data in real time is computationally intensive, and limited resources, such as computational power, sensor capabilities, and budget, may constrain the extent to which a system can be monitored comprehensively. To address this, this article develops a particle-based approach that dynamically selects a subset of sensors that provides the highest information about the state of the system in real time. The proposed approach first predicts the amount of uncertainty in the estimation of the state of the system given noisy measurements from different subsets of available sensors. Then, it selects the sensors that reduce this uncertainty the most, enhancing the precision and efficiency of the monitoring process. The efficacy of the proposed framework is demonstrated via two coupled multidisciplinary systems in the numerical experiments.