G
Graeme Manson
Researcher at University of Sheffield
Publications - 101
Citations - 3901
Graeme Manson is an academic researcher from University of Sheffield. The author has contributed to research in topics: Artificial neural network & Structural health monitoring. The author has an hindex of 29, co-authored 100 publications receiving 3519 citations. Previous affiliations of Graeme Manson include Gyeongsang National University & University of Manchester.
Papers
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Damage detection using outlier analysis
TL;DR: The concept of discordancy from the statistical discipline of outlier analysis is used to signal deviance from the norm in a statistical method for damage detection.
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The fundamental axioms of structural health monitoring
TL;DR: In this paper, the authors explicitly state and justify structural health monitoring axioms, and stimulate discussion and thought within the community regarding these axiomatizations, in order to facilitate new researchers in the field a starting point that alleviates the need to review the vast amounts of literature in this field.
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The application of machine learning to structural health monitoring
Keith Worden,Graeme Manson +1 more
TL;DR: The object of this paper is to illustrate the utility of the data-driven approach to damage identification by means of a number of case studies.
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Experimental validation of a structural health monitoring methodology: part i. novelty detection on a laboratory structure
TL;DR: In this paper, a structural health monitoring methodology for a wing box is presented. Butler et al. used novelty detection based on measured transmissibilities from the structure of the wing.
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Experimental validation of a structural health monitoring methodology: part ii. novelty detection on a gnat aircraft
TL;DR: In this article, the second phase of an experimental validation program for a structural health monitoring methodology based on novelty detection is described, in which damage is introduced by making several copies of an inspection panel, each with a different controlled fault.