In this paper, a new strategy for vibration-based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented structures, where star-topology monitoring systems are typically unsuitable. In particular, an efficient data management procedure is proposed to reduce data transmission, thus improving efficiency and minimizing maintenance interventions for battery replacement in wireless applications. The maximum volume of transmitted data can be selected by the user, based on the specific requirements of the network. Although the considerable reduction of data size, the proposed approach enables accurate estimation of the structural parameters in challenging scenarios where other techniques generally fail. Modal parameters are identified in an online fashion, enabling near real-time detection and localization of early damage. Applications to a real case study instrumented with a dense sensor network show the effectiveness of the proposed approach and the possibility of localizing structural defects in slightly damaged civil structures.

中文翻译：

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使用线拓扑传感器网络瞬时识别密集仪器结构

在本文中，提出了一种基于振动的结构健康监测的新策略，专为具有边缘计算能力的智能传感器设计，该传感器具有以线拓扑组织的边缘计算能力。该解决方案旨在最大限度地优化资源，并能够识别模态参数，即使对于星形拓扑监测系统通常不适合的大型或密集仪器结构也是如此。特别是，提出了一种有效的数据管理程序来减少数据传输，从而提高效率并最大限度地减少无线应用中电池更换的维护干预。用户可以根据网络的具体要求选择最大传输数据量。虽然数据量大大减少，所提出的方法能够在其他技术通常失败的具有挑战性的场景中准确估计结构参数。模态参数以在线方式识别，实现近乎实时的早期损伤检测和定位。使用密集传感器网络进行的真实案例研究的应用表明了所提出方法的有效性以及在轻微损坏的土木结构中定位结构缺陷的可能性。