Xavier Maldague
http://mivim.gel.ulaval.ca
Infrared thermography for NDT:
Applications and more
Chaire de recherche du CanadaTitulaire : Xavier Maldague
Xavier Maldague
November 2013
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Outline
1. Infrared vision;
2. Non-Thermal infrared NDT;
3. Thermal infrared NDT: Passive thermography;
4. Thermal infrared NDT : Active thermography;
5. Thermal infrared NDT : More Applications;
6. Conclusions.
4
Infrared vision
� Infrared vision is a
subdivision of computer
vision (i.e. the use of
computers to emulate
human vision), which
employs (low-, mid- and
high-level) computerized
processes to "make sense"
of images generated in the
infrared part of the
electromagnetic (EM)
spectrum.
Thermal emissions
Non-thermal reflections
Reflectography/
transmittography
Thermography
THzTerahertz imaging
Electromagnetic
spectrum
Planck’s Law
5
Planck’s Law
-2 .3 .8 3 5 8 14 400 400010
-10
10-5
100
105
1010
λ [µm]
Nλ,
b [W
/ m
2 µ
m]
40,000 K: Blue star
5,800 K: The Sun
3,000 K: Common light bulb (100 W)
800 K: An object starts to shine
310 K: Human body's normal temperature
273.15 K: 0oC
2.735 K: Background Cosmic Radiation9.3 µm
visible midIR
longIR
Wien
displacement
law
1 µm
Lo
i d
e W
ien
2897/6000 ~ 0.5 µm
6
Infrared
thermograms
Infrared
thermograms
Visible
photographs
Visible
photographs
Planck’s Law
Self-emissionSelf-emission
Need
illumination
Need
illumination
IR vision: summary of characteristics
�NIR (0.7-1.1 µm) or
SWIR(1.1-2.5 µm)
� Reflections/transmissions
� Single images
�No advanced processing
required
�Qualitative results
�MWIR (3-5 µm) or
LWIR (7-14 µm)
� Thermal emissions
� 3D thermogram sequences
� Advanced processing
techniques
�Qualitative and quantitative
9
Non-thermal vision Thermal vision
NIR reflectography/transmittography of GFRP
11
900 – 1700 nm
NIR: reflection NIR: transmissionvisible images
GFRP: Glass Fiber Reinforced Plastic
front
back
13
Passive thermography: introduction
� The passive approach is used when the object of
interest has enough thermal contrast with
respect to the background in order to be
detected with an infrared sensor. Typical
applications include: surveillance, people
tracking, humidity assessment in buildings,
liquid levels in storage tanks, insulation
problems, electrical components, etc.
Sources : http://www.x20.org/thermal/ http://www.temperatures.com/thermalimaging.html
Aeronautical application: (1/4)
Water ingress detection in honeycomb
14
AA BB CC DD
EE FF GG HH
frontbackVolume, V
[ml]
F 1 0.2
B 1 0.4
G 1 0.6
C 1 0.8
E 10 2
D 10 4
H 10 6
A 10 9
Cells filled
with water
Defective
area
Section of a military
aircraft component
10 cells with 9 ml of water
not frozen frozen
Aeronautical application: (2/4)
Water ingress detection in honeycomb
� The impact of water volume
15
(a) Early thermogram at t=295 s showing all defects (A to H);
(b) Thermogram at t=518 s showing all defects except defect F;
(c) Thermogram at t=1315 s showing only defects A, D and H.
Aeronautical application: (3/4)
Water ingress detection in honeycomb
� The impact of water volume
16
The sound area (dotted
black line) warms up
following a logarithmic
growth with respect to
time
In the presence of water,
temperature profiles
diverge from logarithmic
behavior (since it takes
longer to warm up
water).
The divergent time for all defects is approximately
the same (roughly around 180 s), regardless of the
water extend and volume
0 200 400 600 800 1000 1200 1400800
1000
1200
1400
1600
1800
2000
2200
2400
2600Temperature profiles, Telops HD
t [s]
T [a
rbitra
ry u
nits
]
Sound area
0.2 ml
0.4 ml
0.6 ml
0.8 ml
2 ml
4 ml
6 ml
9 ml
Aeronautical application: (4/4)
Water ingress detection in honeycomb
�Data correlation
17
0 200 400 600 800 1000 1200 1400-100
0
100
200
300
400
500
600
700
800Temperature profiles, Telops HD
t [s]
T [a
rbitr
ary
un
its]
9 ml
6 ml
4 ml
2 ml
0.8 ml
0.6 ml
0.4 ml
0.2 ml
Time for maximum
contrast = 336.2 s
V = f ( tmax )
Maximun
contrast =
146.8
V = 3.2E-08 t 2.685
R² = 0.9911
0
2
4
6
8
10
0 250 500 750 1000 1250 1500
Wa
ter
ingre
ss v
olu
me
[ml]
Time for maximum contrast [s]
Thermograms at different distances
18
268
269
270
271
272
273
274
275
276
277
278
270
275
280
285
290
295
275
280
285
290
295
275
280
285
290
295
300
305
1.5 m from target 4 m from target
10 m from target 20 m from target
0.4 ml
defect
20
Active thermography for NDT
� Active thermography for NDT is based on the detection and
recording by an infrared camera of thermal radiations emitted by
object surface.
� To detect defects, it is sometimes necessary to destabilize the
object thermal state through heating or cooling (→ active
thermography ).
� The presence of an internal defect reveals itself on surface as a
temperature perturbation above this defect.
Active thermography for NDT
Main advantages:
� Possibility to perform one-sided inspection (in reflection configuration);
� Carried out in real-time;
� Appropriate on most composites materials and multi-layer structures,
including porous materials and industrial lines;
� Relatively unaffected by the object’s geometry, and well adapted for
the inspection of large surfaces.
21
Active thermography for NDT
Main problems:
� Sensible to heating sources (type, duration, location);
� Response time (very fast for metals => need for fast acquisition rates);
� Affected by the object’s surface condition and thickness;
22
24
Advanced signal processing techniques
� Thermal contrast-based techniques (max. contrast, FWHM, etc.)
� Differential Absolute Contrast, DAC
� Thermographic Signal Reconstruction, TSR
� Principal Component Thermography, PCT
� Pulsed Phase Thermography, PPT
( ) ( )te
QT πln
2
1lnln −
=∆
A=USVT
)()()( tTtTtTaSd
−=∆
( ) nn
N
k
Nnkjn tkTtF ImReexp
1
0
)2( +=∆∆= ∑−
=
− π
( )tTt
ttTT ddac ′⋅
′−=∆ )(
012 =
∂
∂⋅−∇
t
TT
α
( )te
QTtT
π+=
0,0
3D diffusion equation
1D solution for a Dirac pulse
25
defect
Sa1
Sa2
Sa3
Sa4
0 1 2 3 4 5 6 -0.2 0 0.2 0.4 0.6
.01 .1 1 6.3
-4
-3
-2
-1
0
1
t [s]
∆T
[o
C]
Sa1
Sa2
Sa3
Sa4
.01 .1 1 6.3
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
t [s]
∆T
[o
C]
Sa1
Sa2
Sa3
Sa4
(a) (b)
Absolute contrast DAC
Ibarra-Castanedo C., Bendada A. and Maldague X. “Image and signal processing
techniques in pulsed thermography,” GESTS Int'l Trans. Computer Science and Engr.,
22(1): 89-100, November 2005 available online: http://www.gests.org/down/2709.pdf.
Example: DAC on CFRP
26
Partial Least-Squares Thermography
Time Series
Data
Scores
Highest variance
Loadings Residuals
PLS
Factor 1
��
�′�
PLS
Factor 2
��
�′�
PLS
Factor 3
��
�′�
PLS
Factor a
��
�′�
. . .
How many factors should be included in the
PLS model? Answer: Cross-validation
27
Partial Least-Squares Thermography
0.00637 s 0.318 s 1.726 s
0.8 mm depth
1.0 mm depth
Raw thermograms with
non-uniform heating
Enhanced with PLST,
after non-uniform
heating suppression
30
Pulsed thermography, PT
�Metal corrosion, crack detection, disbonding, impact damage in
composites, turbine blades, delaminations, porosity, defect
characterization: depth, size, thermal properties, artworks.
Lock-in thermography, LT
32
Phase
permanent regimesine wave heating
• same frequency• temporal shift
Thermal waves
input:
output:
33
Lock-in thermography, LT
� Crack identification, disbonding,
impact damage, cultural heritage
inspection, artworks, cultural
buildings.
35
Eddy current (or Inductive) thermography, ECT
� Crack detection in electro-conductive materials, detection of impact
damage in composites, inspection of soldering joints.
41
Open microcracks in thermally-sprayed-coatings
The coating (~100-200 µm) is
formed by a mixture of Tungsten-
Carbide and Cobalt powder
accelerated and heated in a
plasma jet and sprayed onto a 1
mm thick steel substrate.
12.8
mm
16 mm
~0.8 mm ~0.8 mm
42
Optical PT Optical LT
Burst VT Line-scan ECT
Real crushed core produced
during VT inspection
Paint detached
from the surface
Comparative example: PT, LT, VT, ECT
Inspection of CF-18 rudders (2/3)
� Impact of de-noising with synthetic data
f=0.015 Hz f=0.04 Hz f=1.2 Hz
PPT from raw
pulsed data
PPT from
synthetic
pulsed data
44
Inspection of CF-18 rudders (3/3)
�Depth retrieval with
phase profiles
0 0.2 0.4 0.58-0.05
0
0.05
0.1
0.15
f [Hz]
∆φ
[ra
d]
z1,raw
z2,raw
z1,synt
z2,synt
z1=0.5 mm
z2=2 mm
Sa
z1
z2
45
47
Road and bridges inspection (1/4)
Notre-Dame street
Montreal, Canada
November 4th, 2008
Interstate 35
Minneapolis,
August 3rd, 2007
Viaduc de la Concorde,
Montreal, Canada
September 30th, 2006
48
Road and bridges inspection (2/4)
Reinforcement
using composite
layers
Traditional
inspection with
the "tap testing"
techniqueToutry Bridge, France
Fiber distribution and orientation
� Complex-shaped parts
52
� Fiber orientation measurement
Strength and stiffness
Fiber distribution and orientation: point scan
� Laser point scan experimental setup
53
Laboratory setup
Results show the ellipses major axes
indicating the fiber direction for the
same area at two different positions
rotated 90o
6.37°
-85.19°
Error of 1.5°
Artworks inspection (2/3)
Overlay
Hidden drawingsNIR camera (0.9-1.7 µm)
incandescent lamp 90 V
Visible photograph
Artworks inspection (3/3)
Overlay
Defects
Thermal camera (3-5 µm)
PPT phase f=75 mHz
Visible photograph
58
Conclusions
� Infrared reflectography and transmittography employ the non-
thermal part of the infrared spectrum, where the
opacity/transparency of materials, subjected to a specific infrared
radiation, are exploited to detect internal anomalies in materials.
� Infrared thermography works in the thermal part of the infrared
spectrum under the principle that dissimilar materials provide
different thermal signatures, useful for surface/subsurface defect
detection.
� Passive thermography is typically used in the field of security and
surveillance, biological applications, the inspection of electrical and
electronic components, and buildings among others.
59
Conclusions (cont.)
� Active thermography is widely used in aerospace and automobile
industries and is finding new applications such as the inspection of
bridges and roads and the assessment of artworks and cultural
heritage.
� Data processing techniques are required to enhance contrast, to
improve the spatial resolution and to increase the signal-to-noise ratio
of the infrared signal.
� Continual technological progress in commercial infrared cameras and
computers , as well as the constant development of new processing
techniques, have promoted the appearance of new and innovative
applications for infrared vision.
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