The deployment of Structural Health Monitoring (SHM) systems is a natively interdisciplinary task that involves joint research contributions from sensing technologies, data science and civil engineering. The capability to assess, also from remote stations, the working conditions of industrial plants or the structural integrity of civil buildings is widely requested in many application fields. The technological development aims to continuously provide innovative tools and approaches to satisfy these demands. As a first instance, reliable monitoring strategies are needed to detect structural damages while filtering out environmental noise. Ongoing solutions to tackle these topics are based on the exploitation of highly customized sensing technologies, such as shaped transducers for Acoustic Emission (AE) testing or Micro-Electro-Mechanical System (MEMS) accelerometers for Operational Modal Analysis (OMA) [1]. On the other hand, effective data acquisition and storage techniques must be employed to cope with the heterogeneity of the sensing devices and with the amount of data produced by collecting raw measured signals. Finally, damage detection and prediction tasks should be computed via data-driven algorithms that can complement the model-based alternatives traditionally used in civil engineering. Layered SHM architectures [2] represent straightforward approaches to address the system complexity originated by this interdisciplinary design; however, few real-world implementations have been presented so far in the literature. In this paper, we overcome these limitations by presenting an Internet of Things (IoT)-based SHM architecture for the predictive maintenance of industrial sites and civil engineering structures and infrastructures. The proposed cyber-physical system includes a monitoring layer, that consists of accelerometer-based sensor networks, a data acquisition layer, built on the recent W3C Web of Things standard [3], and a data storage and analytics layer, which leverages distributed database and Machine Learning tools. We extensively discuss the hardware/software components of the proposed SHM architecture, by stressing its advantages in terms of device versatility, data scalability and interoperability support. Finally, the effectiveness of the system is validated on a real-world use-case, i.e., the monitoring of a metallic frame structure located at the SHM research labs of the University of Bologna, Italy, within the MAC4PRO project [4].

中文翻译：

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工业厂房和土木结构的结构健康监测和预测：云架构的传感器

结构健康监测 (SHM) 系统的部署是一项本地跨学科任务，涉及传感技术、数据科学和土木工程的联合研究贡献。许多应用领域都广泛要求能够从远程站评估工厂的工作条件或民用建筑的结构完整性。技术发展旨在不断提供创新工具和方法来满足这些需求。首先，需要可靠的监测策略来检测结构损坏，同时过滤掉环境噪音。解决这些问题的持续解决方案基于高度定制的传感技术的开发，例如用于声发射 (AE) 测试的成形传感器或用于操作模态分析 (OMA) 的微机电系统 (MEMS) 加速度计 [1]。另一方面，必须采用有效的数据采集和存储技术来应对传感设备的异质性以及通过收集原始测量信号产生的数据量。最后，损伤检测和预测任务应该通过数据驱动的算法来计算，这些算法可以补充土木工程中传统使用的基于模型的替代方案。分层 SHM 架构 [2] 代表了解决由这种跨学科设计引起的系统复杂性的直接方法；然而，迄今为止，文献中几乎没有介绍过真实世界的实现。在本文中，我们通过提出基于物联网 (IoT) 的 SHM 架构来克服这些限制，用于工业现场和土木工程结构和基础设施的预测性维护。提议的网络物理系统包括一个监控层，它由基于加速度计的传感器网络、一个数据采集层（建立在最近的 W3C 物联网标准 [3] 上）和一个数据存储和分析层组成，它利用分布式数据库和机器学习工具。我们通过强调其在设备多功能性、数据可扩展性和互操作性支持方面的优势，广泛讨论了所提出的 SHM 架构的硬件/软件组件。最后，系统的有效性在现实世界的用例中得到验证，即，