Papers by Author: Graeme Manson

Paper TitlePage

Authors: Ifigeneia Antoniadou, Nikolaos Dervilis, Robert J. Barthorpe, Graeme Manson, Keith Worden
Abstract: The paper summarises some advanced damage detection approaches used for Structural Health Monitoring (SHM) and Condition Monitoring (CM) of wind turbine systems. In the signal processing part, recent time-frequency analysis methods will be presented and examples of their application on condition monitoring of gearboxes will be given. In the pattern recognition part, examples of damage detection in blades will be used to introduce different algorithms for novelty detection.
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Authors: Keith Worden, Graeme Manson, D.J. Allman
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Authors: Keith Worden, Graeme Manson, Cecilia Surace
Abstract: The object of this paper is to illustrate the use of novelty detection techniques in Structural Health Monitoring (SHM) by the consideration of a number of case studies of varying complexity, from a simple lumped-mass system to an FE model of an offshore structure to an experimental study of an aircraft wing.
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Authors: Graeme Manson, Gareth Pierce, Keith Worden, Daley Chetwynd
Abstract: This paper considers the performance of radial basis function neural networks for the purpose of data classification. The methods are illustrated using a simple two class problem. Two techniques for reducing the rate of misclassifications, via the introduction of an “unable to classify” label, are presented. The first of these considers the imposition of a threshold value on the classifier outputs whilst the second considers the replacement of the crisp network weights with interval ranges. Two network training techniques are investigated and it is found that, although thresholding and uncertain weights give similar results, the level of variability of network performance is dependent upon the training approach
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Authors: Gareth Pierce, Keith Worden, Graeme Manson
Abstract: The application of a multilayer perceptron (MLP) neural network to a damage location problem on a GNAT aircraft wing is considered. The problems associated with effective network training and evaluation are discussed, focussing on ensuring good generalisation performance of the network to the classification of new data. Both conventional Maximum Likelihood and Bayesian Evidence based training techniques are considered and a simple thresholding technique is presented to aid in the rejection of poorly regularised network structures. Examples are presented for an artificial simple 2 class problem (drawn from a Gaussian distribution) and a real 9 class problem on the GNAT aircraft wing.
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Authors: Ifigeneia Antoniadou, Keith Worden, Graeme Manson, Nikolaos Dervilis, S.G. Taylor, Charles R. Farrar
Abstract: The RAPTOR telescope systems are astronomical observatories that operate in remote locations in New Mexico searching for astrophysical transients called gamma-ray bursts. Their operating condition should remain at good levels in order to have accurate observations. Currently, the first component of the RAPTOR telescopes to fail is a capstan driving mechanism that operates in a run-to failure mode. The capstans wear relatively frequently because of their manufacturing material and can cause damage to other more expensive components, such as the drive wheels and the telescope optics. Monitoring the condition of these systems seems a reasonable solution since the unpredictable rate at which the capstans experience wear, in combination with the remote locations and high duty cycles of these telescope systems, make it unprofitable to choose a strategy of replacing the capstans at chosen intervals. Experimental tests of the telescope systems reported here recorded vibration signals during clockwise and counterclockwise rotations, similar to a motion known as "homing-sequence". The Empirical Mode Decomposition (EMD) method in combination with the Hilbert Transform (HT) and a new alternative method for the estimation of the instantaneous features of a signal that applies an energy tracking operator, called Teager-Kaiser Energy operator, and an energy separation algorithm to the data being analysed, are the time-frequency analysis methods used for analysis here.
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Authors: B.C. Lee, Graeme Manson, W.J. Staszewski
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Authors: Graeme Manson, Keith Worden
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Authors: D.L. Tunnicliffe, Graeme Manson, Keith Worden, A. Martin
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