This paper deals with state of the art in structural health monitoring (SHM) methods in offshore and marine structures. Most SHM methods have been developed for onshore infrastructures. Few studies are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorises the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines and so on. Additionally, the capabilities of proposed ideas in recent publications are classified into three main categories: model-based methods, vibration-based methods and digital twin methods. Recently developed novel signal processing and machine learning algorithms are reviewed and their abilities are discussed. Developed methods in vision-based and population-based approaches are also presented and discussed. The aim of this paper is to provide guidelines for selecting and establishing SHM in offshore and marine structures.

中文翻译：

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近海和海洋结构的结构健康监测的最新技术水平

本文介绍了近海和海洋结构中结构健康监测 (SHM) 方法的最新技术。大多数 SHM 方法都是为陆上基础设施开发的。很少有研究可用于在近海和海洋结构中实施 SHM 技术。本文旨在填补这一空白，并强调在近海和海洋结构中实施 SHM 方法所面临的挑战。目前的工作对在石油钻井平台、海上风力涡轮机结构、海底系统、船舶、管道等中建立 SHM 模型的可用技术进行了分类。此外，最近出版物中提出的想法的能力分为三大类：基于模型的方法、基于振动的方法和数字孪生方法。回顾了最近开发的新型信号处理和机器学习算法，并讨论了它们的能力。还介绍和讨论了基于视觉和基于人口的方法中开发的方法。本文的目的是为在近海和海洋结构中选择和建立 SHM 提供指南。