Abstract:
Active infrared thermography is one of the
favourable non-destructive testing and evaluation methods
popularly being used for remote inspection of various
materials/products/components/structures. It captures the
temperature distribution over the test material for predefined
thermal stimulus onto the surface, which is further processed to
detect the sub-surface anomalies/defects hidden inside the test
object. Various attempts have been made by several research
groups to reveal the hidden finer subsurface features with
improved sensitivity and resolution. Present work highlights a
principal component analysis and its extension, robust principal
component analysis to inspect for sub-surface flat-bottomed hole
defects inside a mild steel sample. Further, the proposed data
analysis approaches and their capabilities have been compared on
the temporal thermal experimental sequence for a frequency
modulated incident thermal stimulus. It is clear from the obtained
results that principal component analysis outperforms the robust
principal component analysis in providing the information
regarding the hidden defect details lying deep inside the material
with enhanced signal to noise ratio leading to increased
temperature contrast over the detected sub-surface defects
