  The fundamentals of Fourier Transform are presented, with analytical
solutions derived for Continuous Fourier Transform (CFT) of truncated signals,
to benchmark against Fast Fourier Transform (FFT). Certain artifacts from FFT
were identified for decay curves. An existing method for Infrared Thermography,
Pulse Phase Thermography (PPT), was benchmarked against a proposed method using
polynomial fitting with CFT, to analyse cooling curves for defect
identification in Non-Destructive Testing (NDT). Existing FFT methods used in
PPT were shown to be dependent on sampling rates, with inherent artifacts and
inconsistencies in both amplitude and phase. It was shown that the proposed
method produced consistent amplitude and phase, with no artifacts, as long as
the start of the cooling curves are sufficiently represented. It is hoped that
a collaborative approach will be adopted to unify data in Thermography for
machine learning models to thrive, in order to facilitate automated geometry
and defect recognition and move the field forward.
