Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could influence structural properties and therefore the dynamic response, making it more difficult to detect structural problems via traditional SHM techniques. This paper explores the use of a hierarchical Bayesian model to infer expected soil stiffness distributions at both population and local levels, as a basis to perform anomaly detection, in the form of scour, for new and existing turbines. To do this, observations of natural frequency will be generated as though they are from a small population of wind turbines. Differences between individual observations will be introduced by postulating distributions over the soil stiffness and measurement noise, as well as reducing soil depth (to represent scour), in the case of anomaly detection.

翻译：基于群体的结构健康监测（PBSHM）旨在群体成员之间共享信息。海上风电场可视为名义上相同的风力发电机结构群体，但成员之间存在几何形状、海床条件及温度差异等良性变化。这些因素可能影响结构特性及动力响应，增加传统结构健康监测技术检测结构问题的难度。本文探索使用分层贝叶斯模型推断群体及局部层面的预期土体刚度分布，作为对新建及既有风机进行冲刷形式异常检测的基础。为此，将生成来自小型风机群体的固有频率观测数据，通过假设土体刚度和测量噪声的分布，并在异常检测中模拟土体深度减小（代表冲刷）来引入个体观测差异。