45 citations · 217 across the 37 of their papers we have counts for
26 papers · 1 filter
Inter-turbine Modelling of Wind-Farm Power using Multi-task Learning
Simon M. Brealy, Lawrence A. Bull, Pauline Beltrando +3
Because of the global need to increase power production from renewable energy resources, developments in the online monitoring of the associated infrastructure is of interest to re…
Regularising NARX models with multi-task learning
Sarah Bee, Lawrence Bull, Nikolaos Dervilis +1
A Nonlinear Auto-Regressive with eXogenous inputs (NARX) model can be used to describe time-varying processes; where the output depends on both previous outputs and current/previou…
When does a bridge become an aeroplane?
Tina A. Dardeno, Lawrence A. Bull, Nikolaos Dervilis +1
Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a…
Towards an active-learning approach to resource allocation for population-based damage prognosis
George Tsialiamanis, Keith Worden, Nikolaos Dervilis +1
Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM)…
Cost-informed dimensionality reduction for structural digital twin technologies
Aidan J. Hughes, Keith Worden, Nikolaos Dervilis +1
Classification models are a key component of structural digital twin technologies used for supporting asset management decision-making. An important consideration when developing c…
Active learning for regression in engineering populations: A risk-informed approach
Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi +5
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applic…
Multitask learning for improved scour detection: A dynamic wave tank study
Simon M. Brealy, Aidan J. Hughes, Tina A. Dardeno +4
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…
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
S. M. Smith, A. J. Hughes, T. A. Dardeno +3
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…
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno +6
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental cos…
Towards a population-informed approach to the definition of data-driven models for structural dynamics
G. Tsialiamanis, N. Dervilis, D. J. Wagg +1
Machine learning has affected the way in which many phenomena for various domains are modelled, one of these domains being that of structural dynamics. However, because machine-lea…
A decision framework for selecting information-transfer strategies in population-based SHM
Aidan J. Hughes, Jack Poole, Nikolaos Dervilis +2
Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) system…
When is an SHM problem a Multi-Task-Learning problem?
Sarah Bee, Lawrence Bull, Nikolas Dervilis +1
Multi-task neural networks learn tasks simultaneously to improve individual task performance. There are three mechanisms of multi-task learning (MTL) which are explored here for th…
A Meta-Learning Approach to Population-Based Modelling of Structures
G. Tsialiamanis, N. Dervilis, D. J. Wagg +1
A major problem of machine-learning approaches in structural dynamics is the frequent lack of structural data. Inspired by the recently-emerging field of population-based structura…
On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation
G. Tsialiamanis, D. Wagg, N. Dervilis +1
A major problem of structural health monitoring (SHM) has been the prognosis of damage and the definition of the remaining useful life of a structure. Both tasks depend on many par…
Mitigating sampling bias in risk-based active learning via an EM algorithm
Aidan J. Hughes, Lawrence A. Bull, Paul Gardner +2
Risk-based active learning is an approach to developing statistical classifiers for online decision-support. In this approach, data-label querying is guided according to the expect…
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes
Tina A. Dardeno, Lawrence A. Bull, Nikolaos Dervilis +1
Reductions in natural frequency are often used as a damage indicator for structural health monitoring (SHM) purposes. However, fluctuations in operational and environmental conditi…
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers
Aidan J. Hughes, Paul Gardner, Lawrence A. Bull +2
Gaining the ability to make informed decisions on operation and maintenance of structures provides motivation for the implementation of structural health monitoring (SHM) systems.…
On statistic alignment for domain adaptation in structural health monitoring
Jack Poole, Paul Gardner, Nikolaos Dervilis +2
The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning - specifically in the form of domain adapta…
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings
Christopher A. Lindley, Scott Beamish, Rob Dwyer-Joyce +2
It has been shown that ultrasonic techniques work well for online measuring of circumferential oil film thickness profile in journal bearings; unfortunately, they can be limited by…
On generative models as the basis for digital twins
G. Tsialiamanis, D. J. Wagg, N. Dervilis +1
A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account f…
On an application of graph neural networks in population based SHM
G. Tsialiamanis, C. Mylonas, E. Chatzi +3
Attempts have been made recently in the field of population-based structural health monitoring (PBSHM), to transfer knowledge between SHM models of different structures. The attemp…
On generating parametrised structural data using conditional generative adversarial networks
G. Tsialiamanis, D. J. Wagg, N. Dervilis +1
A powerful approach, and one of the most common ones in structural health monitoring (SHM), is to use data-driven models to make predictions and inferences about structures and the…
On the application of generative adversarial networks for nonlinear modal analysis
G. Tsialiamanis, M. D. Champneys, N. Dervilis +2
Linear modal analysis is a useful and effective tool for the design and analysis of structures. However, a comprehensive basis for nonlinear modal analysis remains to be developed.…
Modelling variability in vibration-based PBSHM via a generalised population form
Tina A Dardeno, Lawrence A Bull, Robin S Mills +2
Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen i…