智能传感器对结构健康监测系统及未来资产管理实践的影响。

Preethichandra D M G, Suntharavadivel T G, Kalutara Pushpitha, Piyathilaka Lasitha, Izhar Umer

School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia.

School of Science, Technology and Engineering, Moreton Bay Campus, University of the Sunshine Coast, Moreton Parade, Petrie, QLD 4502, Australia.

Sensors (Basel). 2023 Oct 6;23(19):8279. doi: 10.3390/s23198279.

Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally.

网络传感器和智能传感器的最新发展显著改变了结构健康监测（SHM）和资产管理的实施方式。由于传感器网络持续提供来自被监测结构的实时数据，它们构成了该结构实际状态的更真实图像，据此可根据实际需求安排维护或维修工作。本综述旨在提供丰富的知识，内容涵盖结构健康监测中常用传感器的工作原理，以及用于结构健康监测的基于人工智能的数字孪生系统，并提出一个新的资产管理框架。本文的结构形式同样适合传感器领域以及资产管理领域的研究人员和实践专家。