Modelling variability in vibration-based PBSHM via a generalised population form

DARDENO, TINA A.; Bull, Larry; Mills, Robin S.; Dervilis, Nikolaos; Worden, Keith

doi:10.1016/j.jsv.2022.117227

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…In the OW setting, whilst some older wind turbines may have a rich history of data, newer wind turbines may not, and so independent models may lead to unreliable predictions. On the other end of the spectrum, a complete-pooling approach considers all population data from a single source [9]; this may lead to poor generalisation, particularly when there are significant differences between individual or groups of wind turbines (wind farms). Hierarchical (partial-pooling) models represent a middleground which can be used to learn separate models for each group while encouraging task parameters to be correlated.…”

Section: Hierarchical Bayesian Modelmentioning

confidence: 99%

“…This work adopts a hierarchical Bayesian modelling approach (recently used in the application of PBSHM [8,9]), to develop population and individual turbine-level distributions for the natural frequency of the first bending mode of the structures, which share a level of information between each other. Observations of the natural frequencies are generated via a finite element model of the structure, using soil stiffnesses from predefined distributions as inputs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Anomaly Detection in Offshore Wind Turbine Structures Using Hierarchical Bayesian Modelling

SMITH,

HUGHE,

DARDENO

et al. 2023

Proceedings of the 14th International Workshop on Structural Health Monitoring

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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.

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Section: Hierarchical Bayesian Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%