Multitask learning for improved scour detection: A dynamic wave tank study

TL;DR

This paper introduces a Bayesian hierarchical multitask learning approach to improve scour detection in offshore wind turbines by leveraging population data and surrogate models for more robust anomaly identification.

Contribution

It develops a Bayesian hierarchical model that jointly infers foundation stiffness across structures, enhancing scour detection over traditional methods.

Findings

Hierarchical model improves anomaly detection robustness.

Synthetic and experimental data validate the approach.

Surrogate FE models enable efficient parameter inference.

Abstract

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 Bayesian hierarchical model as a means of multitask learning, to infer foundation stiffness distribution parameters at both population and local levels. To do this, observations of natural frequency from populations of structures were first generated from both numerical and experimental models. These observations were then used in a partially-pooled Bayesian hierarchical model in tandem with surrogate FE models of the structures to infer foundation stiffness parameters. Finally, it is demonstrated how the learned parameters may be used as a basis to perform more robust anomaly detection (as compared to a no-pooling approach) e.g. as a result of scour.