Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling

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Bibliographic Details
Main Authors:	Smith, S. M., Hughes, A. J., Dardeno, T. A., Bull, L. A., Dervilis, N., Worden, K.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
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author	Smith, S. M. Hughes, A. J. Dardeno, T. A. Bull, L. A. Dervilis, N. Worden, K.
author_facet	Smith, S. M. Hughes, A. J. Dardeno, T. A. Bull, L. A. Dervilis, N. Worden, K.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2402_19295
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling Smith, S. M. Hughes, A. J. Dardeno, T. A. Bull, L. A. Dervilis, N. Worden, K. Machine Learning 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.
title	Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
topic	Machine Learning
url	https://arxiv.org/abs/2402.19295

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