Actively managed battery degradation of wireless sensors for structural health monitoring
Actively managed battery degradation of wireless sensors for structural health monitoring
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DOI：
10.1117/12.2658497
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发表时间：
2023-04
期刊：
影响因子：
64.5
通讯作者：
Tahsin Afroz Hoque Nishat;Jong-Hyun Jeong;H. Jo;Qiang Zhou;Jian Liu
中科院分区：
文献类型：
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作者：
Tahsin Afroz Hoque Nishat;Jong-Hyun Jeong;H. Jo;Qiang Zhou;Jian Liu
电池供电的无线传感器网络 (WSN) 因其成本低且易于安装而成为结构健康监测 (SHM) 应用的一种有前途的解决方案。然而，长期的 WSN 运行面临着各种问题，涉及无线传感器的不均匀电池退化、相关电池管理和更换要求，以及在实践中确保 WSN 所需的服务质量 (QoS)。电池寿命是无线传感器网络长期运行的最大限制因素之一。考虑到更换电池的维护成本高昂，系统级缺乏有效的电池退化管理可能会导致 WSN 运行失败。而且，在各种实际的不确定性下，还需要保证QoS。在不确定性情况下，无线传感器网络中最大节点数的最优选择是确保所需服务质量的关键任务。本研究提出了一种基于强化学习（RL）的框架，用于在无线传感器网络系统级别主动控制电池退化，目的是在更换电池组的同时延长使用寿命并确保无线传感器网络的服务质量。考虑到各种实际的不确定性，在现实生活中的 WSN 设置（即斜拉桥 SHM 的 WSN）中开发了综合仿真环境。强化学习代理在开发的强化学习环境下进行训练，以学习最佳节点和占空比，同时在网络级别管理电池健康状况。在本研究中，提出了一种基于振型的质量指数用于演示。训练和测试结果表明，所提出的框架在实现 SHM 无线传感器网络的有效电池健康管理方面具有突出作用。
The battery-powered wireless sensor network (WSN) is a promising solution for structural health monitoring (SHM) applications because of its low cost and easy installation capability. However, the long-term WSN operation suffers from various concerns related to uneven battery degradation of wireless sensors, associated battery management, and replacement requirement, and ensuring desired quality of service (QoS) of the WSN in practice. The battery life is one of the biggest limiting factors for long-term WSN operation. Considering the costly maintenance trips for battery replacement, a lack of effective battery degradation management at the system level can lead to a failure in WSN operation. Moreover, the QoS needs to be ensured under various practical uncertainties. Optimal selection with a maximal number of nodes in WSN under uncertainties is a critical task to ensure the desired QoS. This study proposes a reinforcement learning (RL) based framework for active control of the battery degradation at the WSN system level with the aim of the battery group replacement while extending the service life and ensuring the QoS of WSN. A comprehensive simulation environment was developed in a real-life WSN setup, i.e. WSN for a cable-stayed bridge SHM, considering various practical uncertainties. The RL agent was trained under a developed RL environment to learn optimal nodes and duty cycles, meanwhile managing battery health at the network level. In this study, a mode shape-based quality index is proposed for the demonstration. The training and test results showed the prominence of the proposed framework in achieving effective battery health management of the WSN for SHM.