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Found 15 papers, 1 papers with code
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection.
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
Population-based structural health monitoring (PBSHM), aims to share information between members of a population.
Population-based wind farm monitoring based on a spatial autoregressive approach
Population-based structural health monitoring can further reduce the cost of health monitoring systems by implementing one system for multiple structures (i. e.~turbines).
Towards a population-informed approach to the definition of data-driven models for structural dynamics
The current work is aimed at motivating the use of models which learn such relationships from a population of phenomena, whose underlying physics are similar.
On the hierarchical Bayesian modelling of frequency response functions
In this paper, a combined probabilistic FRF model is developed for a small population of nominally-identical helicopter blades, using a hierarchical Bayesian structure, to support information transfer in the context of sparse data.
Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets
There have been recent efforts to move to population-based structural health monitoring (PBSHM) systems.
A Meta-Learning Approach to Population-Based Modelling of Structures
The approach followed here is meta-learning, which is developed with a view to creating neural network models which are able to exploit knowledge from a population of various tasks to perform well in newly-presented tasks, with minimal training and a small number of data samples from the new task.
On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation
The model is able to perform in a population-based SHM (PBSHM) framework, to take into account many past states of the damaged structure, to incorporate uncertainties in the modelling process and to generate potential damage evolution outcomes according to data acquired from a structure.
On generative models as the basis for digital twins
Two different types of generative models are considered here.
On generating parametrised structural data using conditional generative adversarial networks
The cGAN is trained on data for some discrete values of the temperature within some range, and is able to generate data for every temperature in this range with satisfactory accuracy.
On partitioning of an SHM problem and parallels with transfer learning
A second approach to the problem is considered by adopting ideas from transfer learning (usually applied in much deeper) networks to see if a network trained on the simpler damage cases can help with feature extraction in the more difficult cases.
On an application of graph neural networks in population based SHM
The proposed approach is tested in a simulated population of trusses.
On the application of generative adversarial networks for nonlinear modal analysis
The method is tested on simulated data from structures with cubic nonlinearities and different numbers of degrees of freedom, and also on data from an experimental three-degree-of-freedom set-up with a column-bumper nonlinearity.
Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm
Power curves capture the relationship between wind speed and output power for a specific wind turbine.
On risk-based active learning for structural health monitoring
A primary motivation for the development and implementation of structural health monitoring systems, is the prospect of gaining the ability to make informed decisions regarding the operation and maintenance of structures and infrastructure.
