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Found 25 papers, 4 papers with code
Quantifying the value of information transfer in population-based SHM
Population-based structural health monitoring (PBSHM), seeks to address some of the limitations associated with data scarcity that arise in traditional SHM.
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs.
A decision framework for selecting information-transfer strategies in population-based SHM
Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) systems.
When is an SHM problem a Multi-Task-Learning problem?
Multi-task neural networks learn tasks simultaneously to improve individual task performance.
Towards risk-informed PBSHM: Populations as hierarchical systems
The current paper presents a formal representation of populations of structures, such that risk-based decision processes may be specified within them.
On topological data analysis for structural dynamics: an introduction to persistent homology
In this work, a method of quantifying the shape of data, via a topic called topological data analysis will be introduced.
On topological data analysis for SHM; an introduction to persistent homology
The main tool within topological data analysis is persistent homology; this is a means of measuring the shape of data, from the homology of a simplicial complex, calculated over a range of values.
A topological analysis of cointegrated data: a Z24 Bridge case study
Cointegration is used in this paper to remove effects from Environmental and Operational Variations, by cointegrating the first four natural frequencies for the Z24 Bridge data.
Mitigating sampling bias in risk-based active learning via an EM algorithm
For SHM applications, the value of information is evaluated with respect to a maintenance decision process, and the data-label querying corresponds to the inspection of a structure to determine its health state.
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes
In this paper, an overlapping mixture of Gaussian processes (OMGP), was used to generate labels and quantify the uncertainty of normal-condition frequency response data from the helicopter blades.
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers
For risk-based active learning in SHM, the value of information is evaluated with respect to a maintenance decision process, and the data-label querying corresponds to the inspection of a structure to determine its health state.
On statistic alignment for domain adaptation in structural health monitoring
The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data.
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings
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 their measuring range and unable to capture details of the film all around the bearing circumference.
Modelling variability in vibration-based PBSHM via a generalised population form
This work aims to address this variability by defining a general model for the frequency response functions of the blades, called a form, using mixtures of Gaussian processes.
On robust risk-based active-learning algorithms for enhanced decision support
In contrast, the discriminative classifiers are shown to have excellent robustness to the effects of sampling bias.
Canonical-Correlation-Based Fast Feature Selection
The supporting theorems developed for the feature selection method are fundamental to the understanding of the canonical correlation analysis.
Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces
One of the requirements of the population-based approach to Structural Health Monitoring (SHM) proposed in the earlier papers in this sequence, is that structures be represented by points in an abstract space.
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data
In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete.
Damage detection in operational wind turbine blades using a new approach based on machine learning
The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs).
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves
The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monitoring (SHM).
A probabilistic risk-based decision framework for structural health monitoring
Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monitoring (SHM) systems.
Decision Making
Decision Making Under Uncertainty
Applications
A Bayesian methodology for localising acoustic emission sources in complex structures
In the field of structural health monitoring (SHM), the acquisition of acoustic emissions to localise damage sources has emerged as a popular approach.
Bayesian graph neural networks for strain-based crack localization
A common shortcoming of vibration-based damage localization techniques is that localized damages, i. e. small cracks, have a limited influence on the spectral characteristics of a structure.
Computational Engineering, Finance, and Science Data Analysis, Statistics and Probability
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
The problem of discovering governing equations is cast as that of selecting relevant variables from a predetermined dictionary of basis variables and solved via sparse Bayesian linear regression.
Model Selection
Variable Selection
Methodology
Systems and Control
Systems and Control
Applications
Considering discrepancy when calibrating a mechanistic electrophysiology model
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions.
