# Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling

arXiv: https://arxiv.org/abs/2402.19295 · HTML: https://mlsanity.com/paper/2402.19295/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Towards an active-learning approach to resource allocation for population-based damage prognosis](https://mlsanity.com/paper/2409.18572/) (2024)
- [Latent variable models for simultaneous EOV identification and removal in population-based SHM](https://mlsanity.com/paper/2608.11995/) (2026)

## Abstract (arXiv)

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.