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Title: Automated analysis of non destructive evaluation data
Author: Connor, Andrew James
ISNI:       0000 0004 2703 6089
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2011
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Interpretation of NDE data can be unreliable and difficult due to the complex interaction between the instrument, object under inspection and noise and uncertainties about the system or data. A common method of reducing the complexity and volume of data is to use thresholds. However, many of these methods are based on making subjective assessments from the data or assumptions about the system which can be source of error. Reducing data whilst retaining important information is difficult and normally compromises have to be made. This thesis has developed methods that are based on sound mathematical and scientific principles and require the minimum use of assumptions and subjective choices. Optimisation has been shown to reduce data acquired from a multilayer composite panel and hence show the ply layers. The problem can be ill-posed. It is possible to obtain a solution close to optimum and obtain confidence on the result. Important factors are: the size of the search space, representation of the data and any assumptions and choices made. Further work is required in the use of model based optimisation to measure layer thicknesses from a metal laminate panel. A number of important factors that must be addressed have been identified. Two novel approaches to removing features from Transient Eddy-Current (TEC) data have been shown to improve the visibility of defects. The best approach to take depends on the available knowledge of the system. Principal Value Decomposition (PVD) has been shown to remove layer interface reflections from ultrasonic data. However, PVD is not suited to all problems such as the TEC data described. PVD is best suited in the later stages of data reduction. This thesis has demonstrated new methods and a roadmap for solving multivariate problems, these methods may be applied to a wide range of data and problems.
Supervisor: Forrest, Andrew ; Simonetti, Francesco Sponsor: QinetiQ ; RCNDE ; EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral