Adaptive sensor steering strategy using deep reinforcement learning for dynamic data acquisition in digital twins

Abstract This paper introduces a sensor steering methodology based on deep reinforcement learning (DRL) to enhance the predictive accuracy and decision support capabilities of digital twins by optimizing the data acquisition process. Traditional sensor placement techniques are often constrained by one-off optimization strategies, which limit their applicability for online applications requiring continuous informative data assimilation. The proposed approach addresses this limitation by offering an adaptive framework for sensor placement within the digital twin paradigm. The sensor placement problem is formulated as a Markov decision process (MDP), enabling the training and deployment of an agent capable of dynamically repositioning sensors in response to the evolving conditions of the physical structure as represented by the digital twin. This ensures that the digital twin maintains a highly representative and reliable connection to its physical counterpart. The proposed framework is validated through a series of comprehensive case studies involving a cantilever plate structure subjected to diverse conditions, including healthy and damaged conditions. The results demonstrate the capability of the DRL agent to adaptively reposition sensors, improving the quality of data acquisition and hence enhancing the overall accuracy of digital twins.