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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2402.19295 |
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| _version_ | 1866916401883643904 |
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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 |