Abstract In this work, investigations have been conducted on a novel technique for enhancing thermographic data, which is known as thermographic signal reconstruction, to assess its defect detection limitation on CFRP composite. For defect greater than 4mm diameter, results have indicated a 60% improvement in detectability

D P Almond

S Quek

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 H.15.1
Defect Detection Capability of Pulsed Transient 
Thermography  
 
by S. Quek, and D.P. Almond 
 
UK Research Centre in NDE, Department of Engineering and Applied Science, 
University of Bath, Bath, BA2 7AY. 
e-mail: D.P.Almond@bath.ac.uk 
 
Abstract 
 
In this work, investigations have been conducted on a novel technique for enhancing 
thermographic data, which is known as thermographic signal reconstruction, to 
assess its defect detection limitation on CFRP composite. For defect greater than 
4mm diameter, results have indicated a 60% improvement in detectability. 
 
1. Introduction 
 
Pulsed thermography is gaining wide acceptance in the aerospace, automotive 
and power generation industries, owing to its quickness of inspection, repeatability 
and sensitivity. One commercially available system is the ThermoScope developed 
by Thermal Wave Imaging Inc (TWI) in USA. It consists of a central control unit, an 
imaging head and a complete suite of image acquisition and processing software.  
The very essence of the system is the thermographic signal reconstruction 
(TSR) algorithm, which allows significant enhancement of the acquired data 
sequence. The process yields three types of images, namely: synthetic image and 
first and second time derivative images. These images reveal smaller and/or deeper 
defects or subtle features that are undetectable in the raw images and are reported 
by TWI in several publications [1-4]. The objective of this work is to make a 
quantitative assessment of the defect detection capabilities of these imaging 
processes. 
 
2. Principles of Pulsed Thermography and TSR Technique  
 
During inspection, a brief and high intensity flash is applied to heat up the 
sample. The decay of the sample surface temperature is detected and acquired by 
an infrared camera. Each image in the acquired sequence corresponds to the 
surface temperature at a particular time. Any subsurface anomaly will give rise to a 
local temperature increase and therefore resulting in thermal contrast [5]. Typically, 
the logarithmic time histories of the pixels in the anomalous and sound regions have 
the profiles as depicted by figure 1 [6].  
In the TSR process, a polynomial function that has the form as equation 1 is 
fitted to the logarithmic time history of each individual pixel in the raw data. 
Subsequently, the first and second time derivatives are calculated using equation 2 
and 3.  
 
n
n
n
n
n tatatataataT )(ln)(ln)(lnln)(lnln
3
3
2
210
0
+++++== ∑
∞
=
                   (1) 
1
21
1
1 )(lnln2)(ln
ln
ln −∞
=
− +++== ∑ nn
n
n
n tnataatnatd
Td                                  (2) 
http://dx.doi.org/10.21611/qirt.2004.024
 H.15.2
2
32
2
2
2
2
)(ln)1(ln62))(ln1(
)(ln
)(ln −∞
=
− −+++=−= ∑ nn
n
n
n tanntaatnnatd
Td           (3) 
 
This technique has the following advantages over many conventional approaches in 
processing thermographic data: 
 
1. It retains the fidelity of the low frequency thermal event and accentuates the 
signal contributed by a subsurface anomaly whilst suppressing high frequency 
temporal noise and non-thermal events. 
 
2. It is also an effective data compression method by which the entire acquired 
data sequence can be replaced and recreated using a set of polynomial 
coefficients. The storage space is now reduced from approximately 49MB down 
to 5MB. This in turn facilitates large area inspection, which requires multiple 
image acquisitions at different spots for full coverage [8].  
 
3. Advanced mathematical operations such as FFT and differentiation can now be 
performed quickly without having the undesired noise problems [8]. 
 
3. Samples and Experimental Procedures 
 
The state-of-the-art Thermoscope was utilised for the experimental work. Its 
imaging head consists of a flash heating system (power output from lamp is 2kJ in 2 
to 5ms) and a medium wave infrared camera (Merlin 3-5µm by Indigo), which uses a 
cooled indium antimonide detector with a frame rate of 60Hz and a focal plane array 
pixel format of 320(H)×256(V). 
 
Two sets of test-pieces containing back-drilled cylindrical hole defects at varied 
depths were used. The first consisted of aluminium, glass fibre reinforced plastic 
(GFRP) and mild steel samples and the second was a total of 4 carbon fibre 
reinforced plastic (CFRP) composite samples. The first provided a qualitative 
validation on the applicability and repeatability of the TSR technique for different 
engineering materials while the second allowed a quantitative assessment of its 
limitation on defect detection for CFRP composite.   
 
Table 1.  Relevant information for each of the composite samples 
Items Sample 1 Sample 2 Sample 3 Sample 4 
Thickness (mm) 7 7 7 14 
Diameter (mm) 2 4 6 10 
Depths (mm) 0.25, 0.5, 0.75, 1 1.25, 1.5, 1.75, 2 
0.50, 1.0, 1.50, 2 
2.25, 2.5, 2.75, 3 
2.5, 2.75, 3, 3.25 
3.5, 3.75, 4, 4.25 
3, 4.0, 4.5,  
5, 5.5, 6.0 
 
Each of the samples was placed beneath the imaging head and adjusted to align 
with the focal plane of the infrared camera. Immediately after pulsed heating the 
sample, image acquisition was automatically executed by the system at a preset 
frame rate chosen for that particular sample. It was necessary since each of the 
samples had distinct thermal properties and contained defect(s) with varied 
diameter(s) and depth(s).  A frame rate of 20Hz was used for the GFRP sample and 
60Hz for both the aluminium and mild steel samples. CFRP samples with 2, 4, 6 and 
10mm defects were acquired at the rate of 20, 7.5, 7.5 and 3.75Hz. All the acquired 
data were further processed using MOSAIC software with the TSR technique. 
 
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 H.15.3
4. Results and Discussions 
 
4.1 Qualitative Assessment 
  
Figure 2, 3 and 4 show the raw and reconstructed images for aluminium, GFRP 
and mild steel samples. From the illustrated figures and by inspecting on a frame-by-
frame basis, the following points are noted: 
 
• The first and second time derivative images allow detection of smaller and deeper 
defects that are undetectable on the raw images.  
 
• The reconstructed (TSR) images also facilitate earlier defect detection. 
 
4.2 Quantitative Assessment 
 
Images for the CFRP sample containing 6mm diameter defects are shown in 
figure 5. The maximum depths for detecting the 2, 4, 6 and 10mm defects on the raw 
and TSR images are presented in figure 6 as the plots of maximum depth of 
detection versus defect diameter respectively. Note that the maximum depth of 
detection for each diameter defect was determined by viewing the movies of the raw 
and TSR sequences, i.e., visual inspection. The abbreviation 1st and 2nd used in the 
following denote the first and second time derivative images respectively. The plot of 
optimum frame rate versus defect diameter is shown in figure 7. The following 
conclusions are drawn from studying the results: 
 
• For 2, 4, 6 and 10mm defects, the corresponding maximum depths of detection are 
1.25, 2, 2.5 and 3.35mm on the raw images and are 1.75 (1st), 3 (1st and 2nd), 4 (1st 
and 2nd) and 5.34mm (1st and 2nd) on the TSR images. This in turn results in 40, 
50, 60 and 60% improvement in detectability for each diameter defect.. 
 
• For raw images, the diameter-to-depth ratio estimated for each diameter defect is 
1.6, 2, 2.4 and 2.98. These ratios, except for the case of 2mm defect, conform to 
the practical rule of thumb, which specifies the minimum size for defect detection 
on CFRP is that the defect diameter-to-depth ratio must be greater than 2. 
Nonetheless, for reconstructed images, the diameter-to-depth ratios are 1.14, 1.33, 
1.50 and 1.87 for each diameter defect and this therefore demonstrates the 
significant enhancement of the TSR process. 
 
• One important finding of this experiment is that in order to detect a smaller and/or 
deeper defect, which is undetectable on the raw images, on the TSR images, an 
optimum frame rate must be used for image acquisition. Using frame rate other 
than the optimum frame rate will result in no improvement with the TSR images in 
detecting deeper defect, i.e., obtaining same results as the raw images.  
 
• Another important observation from figure 7 is that the optimum frame rate is 
significantly higher for 2mm defect than the others. This is the case because as 
compared with larger defects, the contrast of smaller defects disappear in a short 
period of time [7]. Therefore, a higher frame rate is required to acquire the transient 
phenomenon before it becomes undetectable. 
 
 
 
http://dx.doi.org/10.21611/qirt.2004.024
 H.15.4
Conclusion 
 
The studies conducted in this work have established the detection limitation of 
the TSR technique on CFRP. For defects greater than 4mm diameter, which are 
common in aerospace industry, a 60% improvement in detectability is achievable 
with the TSR process. Nonetheless, such improvement is only possible provided that 
the right frame rate is applied for image acquisition in the first instance. By contrast, 
the improvement is less for 2mm diameter defect as the need to use higher frame 
has resulted in a lower signal-to-noise ratio. 
 
REFERENCES 
 
[1] Shepard S.M., Ahmed T., Rubadeux B.A., Wang D. and Lhota J.R. – Synthetic 
Processing of Pulsed Thermographic Data for Inspection of Turbine 
Components, British Inst. of NDT, Insight, Vol. 43, No. 9, Sept 2001, p. 587-589.  
 
[2] Shepard, S.M. – Temporal Noise Reduction, Compression and Analysis of 
Thermographic Image Data Sequences, US Patent 6,516,084, Feb 2003. 
 
[3] Shepard S.M., Lhota J.R., Ahmed T. and Yu Lin Hou – Thermographic 
Inspection of Composite Structures, SAMPE Journal, Vol. 39, No. 5, Sep/Oct 
2003. 
 
[4] Shephard S.M., Lhota J.R., Rubadeux B.A., Wang D. and Ahmed T. – 
Reconstruction and Enhancement of Active Thermographic Image Sequences, 
Optical Engineering, Vol. 42, No. 5, SPIE, May 2003, p. 1337-1342. 
[5] Almond D. P. and Lau S. K. – Defect sizing by transient thermography. I. An 
analytical treatment, J. Phys. D: Appl. Phys. 27, 1994, p. 1063-1069. 
 
[6] Krapez, Balegeas, Deom and Lepoutre – Early detection by stimulated infrared 
thermography, Advances in Signal Processing for Nondestructive Evaluation of 
Materials, Maldague X.P., Ed., Kluwer Academic Publishers, The Nertherland, 
1994, p. 303-321. 
 
[7] Saintey M.B. and Almond D. P. – Defect sizing by transient thermography. II. A 
Numerical Treatment, J. Phys. D: Appl. Phys. 28, 1995, p. 2539-2546. 
 
[8] Shepard S.M., Lhota J.R., Hou Y. and Ahmed T. – Gold standard comparison of 
thermographic sequence data, British Inst. of NDT, Insight, Vol 46, No. 4, April 
2004. 
 
 
 
 
 
 
 
 
 
 
 
Fig. 1. Logarithmic time histories of the pixels in the anomalous and sound regions. 
10
-1
10
0
10
4.5
10
4.6
10
4.7
Logarithmic Time (s)
Lo
ga
rit
hm
ic
 T
em
pe
ra
tu
re
Defect Region
Sound Region
Logarithmic Time (s) 
Lo
ga
rit
hm
ic
 T
em
pe
ra
tu
re
 
http://dx.doi.org/10.21611/qirt.2004.024
 H.15.5
  
Raw Image Synthetic Image 1
st Time Derivative 
Image 
2nd time Derivative 
Image 
Fig. 2. Raw and TSR images for the aluminium sample contains a total of 12 
defects with different diameters and depths. The 1st and 2nd time derivative  
images have revealed those that are not shown on the raw image. 
 
  
Raw Image Synthetic Image 1
st Time Derivative 
Image 
2nd time Derivative 
Image 
Fig. 3. Raw and TSR images for the GFRP sample contains a total of 12 defects 
with different diameters and depths. Defects that appear blurred on the raw image 
are enhanced and become visible on the 1st and 2nd time derivative images. 
 
  
Raw Image Synthetic Image 1
st Time Derivative 
Image 
2nd time Derivative 
Image 
Fig. 4. Raw and TSR images of mild steel sample contains a total of 9 defects with 
different diameters and depths. Eight of those are detected on the TSR images.  
(Courtesy of TWI).  
 
    3.5       3.75       4        4.25     3.5       3.75       4        4.25     3.5       3.75       4        4.25 
Raw Images 1st Time Derivative Image 2nd Time Derivative Image 
Fig. 5. The raw and TSR image sequences for the CFRP sample with 6mm diameter 
defect at the depths of 3.5, 3.75, 4 and 4.25mm. These defects are not detectable on 
the raw image sequence but the dark spots on the 1st and 2nd time derivative image 
sequences reveal their presence.  
http://dx.doi.org/10.21611/qirt.2004.024
 H.15.6
1.25
3
4.75
2 4 6 8 10
Defect Diameter (mm)
M
ax
. D
ep
th
 o
f D
et
ec
tio
n 
(m
m
)
0
10
20
30
40
50
60
70
D
et
ec
tio
n 
Im
pr
ov
em
en
t (
%
)
M ax Depth o f Detection (TSR)
M ax Depth o f Detection (Raw)
Detection Improvement
 
Fig. 6. The plots of maximum depth of detection versus defect diameter. 
 
20
3.757.57.5
3.75
7.5
11.25
15
18.75
2 4 6 8 10
Defect Diameter (mm)
O
pt
im
um
 S
am
pl
in
g 
Fr
eq
 (H
z)
 
Fig. 7. The plot of optimum frame rate versus defect diameter. 
 
 
http://dx.doi.org/10.21611/qirt.2004.024


Defect detection capability of pulsed transient thermography

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Defect detection capability of pulsed transient thermography

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