87 citations · 257 across the 8 of their papers we have counts for
10 papers
On topological data analysis for SHM; an introduction to persistent homology
Tristan Gowdridge, Nikolaos Devilis, Keith Worden
This paper aims to discuss a method of quantifying the 'shape' of data, via a methodology called topological data analysis. The main tool within topological data analysis is persis…
On partitioning of an SHM problem and parallels with transfer learning
G. Tsialiamanis, D. J. Wagg, P. A. Gardner +2
In the current work, a problem-splitting approach and a scheme motivated by transfer learning is applied to a structural health monitoring problem. The specific problem in this cas…
Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces
George Tsialiamanis, Charilaos Mylonas, Eleni Chatzi +3
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…
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data
Lawrence A. Bull, Paul Gardner, Timothy J. Rogers +3
In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are…
Damage detection in operational wind turbine blades using a new approach based on machine learning
Kartik Chandrasekhar, Nevena Stevanovic, Elizabeth J. Cross +2
The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environment…
A probabilistic risk-based decision framework for structural health monitoring
Aidan J. Hughes, Robert J. Barthorpe, N. Dervilis +2
Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monit…