用深度强化学习动态调整传感器位置，提升数字孪生的预测精度。
Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins
- 将传感器部署建模为马尔可夫决策过程，实现在线自适应调整。
- 在悬臂板结构上验证，能根据损伤状态动态优化数据采集质量。
- 适合需要持续感知的工业数字孪生系统，如智能运维场景。
本文提出一种基于深度强化学习的传感器引导方法，旨在通过优化数据采集过程，提升数字孪生的预测准确性和决策支持能力。传统传感器布置方法常受限于一次性优化策略，难以满足需要持续获取高信息量数据的在线应用需求。所提方法构建了数字孪生范式下的自适应传感器部署框架，将传感器放置问题形式化为马尔可夫决策过程，训练并部署一个能够根据物理结构演化状态动态重定位传感器的智能体。该机制确保数字孪生与物理实体保持高度代表性与可靠性关联。通过一系列针对悬臂板结构的案例研究（涵盖健康与损伤状态）进行验证，结果表明，深度强化学习智能体能有效动态调整传感器位置，显著提升数据采集质量，从而增强数字孪生的整体准确性。
原文摘要 · Abstract (English)
This paper introduces a sensor steering methodology based on deep reinforcement learning to enhance the predictive accuracy and decision support capabilities of digital twins by optimising the data acquisition process. Traditional sensor placement techniques are often constrained by one-off optimisation 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, 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 deep reinforcement learning agent to adaptively reposition sensors improving the quality of data acquisition and hence enhancing the overall accuracy of digital twins.
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