3 research outputs found
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A Novel Defect Depth Measurement Method based on Nonlinear System Identification for Pulsed Thermographic Inspection
This paper introduces a new method to improve the reliability and confidence level of defect depth measurement based on pulsed thermographic inspection by addressing the over-fitting problem. Different with existing methods using a fixed model structure for all pixels, the proposed method adaptively detects the optimal model structure for each pixel thus targeting to achieve better model fitting while using less model terms. Results from numerical simulations and real experiments suggest that (a) the new method is able to measure defect depth more accurately without a pre-set model structure (error is usually within when SNR>32 dB) in comparison with existing methods, (b) the number of model terms should be 8 for signals with SNR∈ [30 dB, 40 dB] 8–10 for SNR>40 dB and 5–8 for SNR<30 dB, and (c) a data length with at least 100 data points and 2–3 times of the characteristic time usually produces the best results
Control of spatio-temporal pattern formation governed by geometrical models of interface evolution
Numerous natural phenomena are characterized by spatio-temporal dynamics which give rise to time evolving spatial patterns. Although studies that address the problem of modelling these complex dynamics exist, a model based control approach for such systems is still a challenging task. The work in this thesis is concerned with the development of control methods for such spatio-temporal systems, where interface growth is represented using a geometric evolution law. In particular, the focus is set on the control of dendritic crystal growth and wind-aided wildfire sprea
Three-dimensional subsurface defect reconstruction for industrial components using pulsed thermography
Pulsed thermography is a promising method for detecting subsurface defects, but
most pulsed thermographic inspection results are represented in the form of 2D
images. Such a representation can limit the understanding of where the defects
initiate and how they grow by time, which is a key to predict the remaining use of
life of component and feedback to the design to avoid such defects. Threedimensional subsurface defect visualisation is a solution that can unlock this
limitation. A straightforward approach to reconstruct 3D subsurface defect is
conducting two inspections on both front and rear sides. However, the
deployment of this approach can be limited because 1) one side of the inspected
component could be inaccessible; 2) the accuracy of measurement could be
compromised if the defect thickness is very thin due to extreme closed values of
defect depths from two inspections; and 3) if the defect is too deep for one side,
the defect could be missed. Addressing the challenge of 3D subsurface defect
reconstruction and visualisation, this thesis proposes a novel technique to
measure defect depth and estimate defect thickness simultaneously through
estimating the thermal wave reflection coefficient value achieved by introducing
a modified heat transfer model based on a single-side inspection method.
The proposed method is validated through model simulations, experimental
studies, and a use case. Four composite samples with different defect types,
sizes, depths and thicknesses, are used for experimental studies; a steel sample
with a ‘s’ shape triangular air-gap inside is used for a use case. The simulation
results show that under the noise level of 25 dB, the percentage error of the
developed depth measurement method is 0.25% whilst the minimum error of the
best existing method is 2.25%. From the experimental study results, the averaged
percentage error of the defect thickness estimation is less than 10% if the defect
thickness is no more than 3 mm. For the use case, the reconstructed defect
shape is similar to the X-ray image.Manufacturin