Investigation of lock-in infrared thermography for evaluation of subsurface defects size and depth

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 16, No. 11, pp. 2255-2264 OCTOBER 2015 / 2255

© KSPE and Springer 2015

Investigation of Lock-in Infrared Thermography for

Evaluation of Subsurface Defects Size and Depth

Shrestha Ranjit1, Kisoo Kang2, and Wontae Kim1,#

1 Department of Mechanical & Automotive Engineering, Kongju National University, 1223-24, Cheonan-daero, Seobuk-gu, Cheonan-si, Chungcheongnam-do, 31080, South Korea2 Rolling Technology Development Team, Technical Research Center, Hyundai Steel Co., 1480, Bukbusaneop-ro, Songak-eup, Dangjin-si, Chungcheongnam-do, 31719, South Korea

# Corresponding Author / E-mail: [email protected], TEL: +82-41-521-9289, FAX: +82-41-555-9123

KEYWORDS: Amplitude image, Finite element analysis, Image processing, Lock-in thermography, Phase image, Signal to noise ratio

In this study, the investigation on lock-in infrared thermography was done for the detection and estimation of artificial subsurface

defects size and depth in stainless steel sample. The experimental and the finite element analysis were performed at several excitation

frequencies to interrogate the sample ranging from 0.182 down to 0.021 Hz. A finite element model using ‘ANSYS 14.0’ was used

to completely simulate the lock-in thermography. The four point method was used in post processing of every pixel of thermal images

using the MATLAB programming language. A signal to noise ratio analysis was performed on both phase and amplitude images in

each excitation frequency to determine the optimum frequency. The relationship of the phase value with respect to excitation frequency

and defect depths was examined. Amplitude image was quantitatively analyzed using Vision Assistant, a special tool in LABVIEW

program to acquire the defects size. The phase image was used to calculate the defects depth considering the thermal diffusivity of

the material and the excitation frequency for which the defects become visible. A finite element analysis result was found to have good

correlation with experimental result and thus demonstrated potentiality in quantification of subsurface defects.

Manuscript received: January 20, 2015 / Revised: May 26, 2015 / Accepted: August 4, 2015

1. Introduction

Stainless steel (STS) is an alloy of iron with a minimum of 10.5%

chromium. Due to its corrosion resistance and strength, STS is an

attractive material for many industrial, architectural, chemical,

consumers and a variety of applications.1 High quality of materials and

structures is an important factor in many areas of human activities.2 A

major effort to reach the high level of quality is to implement various

inspection tasks. Non-destructive testing (NDT) is one of the most

important means to detect and verify the quality of items.3 The

requirements for NDT is driven by the need for low cost methods and

instruments with great reliability, sensitivity, user friendliness, high

operational speed as well for applicability to increasingly complex

materials and structures.4 Temperature is one of the most common

indicators of the structural health of equipment and components. Faulty

machineries, corroded electrical connections and damaged material

components can cause abnormal temperature distribution.5 In this

context, Infrared Thermography (IRT) is an emerging NDT and

evaluation technique that allows the non-contact inspection and

monitoring of systems and materials through a mapping of thermal

patterns on the surface of the objects of interest.6-8 NDT using active

IRT provides information on material, structure, physical & mechanical

properties, discontinuities and defects present on the analyzed

specimen.9

Defect detection principle in IRT is based on the fact that a

difference in thermal properties exists between the sound and a defective

area, which can be used for defect detection and quantification

purposes.10-12 Lock-in thermography (LIT) facilitates better subsurface

defect detection than ordinary infrared thermography because the

thermal wave is very sensitive to interfaces between materials and less

NOMENCLATURE

IRT = Infrared Thermography

FEA=Finite Element Analysis

LIT=Lock-in Thermography

NDT=Non-destructive Testing

SNR=Signal Noise Ratio

STS=Stainless Steel

DOI: 10.1007/s12541-015-0290-z ISSN 2234-7593 (Print) / ISSN 2005-4602 (Online)

2256 / OCTOBER 2015 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 16, No. 11

sensitive to non-uniform emission and surrounding conditions.13 The

concept of LIT was appeared in 1980s concerning electronic device

testing and became popular in the 1990s in connection with the NDT

and evaluation of materials. One of active research by employing IRT

technology is in detecting the defect and estimating the depth of that

defect. G. Busse highlights the significance of using the phase angle as

a measure of depth and further developed a method of subsurface

imaging.14,15 G. Busse then suggested the use of a focal plane infrared

detector together with remote lamps in a thermographic technique.16

Meola et al. have investigated the effects of defect size, depth, and

thickness in composite panels using LIT.17 V.P. Vavilov and R. Taylor

developed an empirical rule; the radius of the smallest defect should be

at least one to two times larger than its depth under the surface.18 X.

Maldague and S. Marinetti developed the Pulse Phase Thermography

(PPT) by combining the advantages of both LIT and Pulse Thermography

without sharing their drawbacks.19,20 Wu and Busse demonstrated that

LIT can eliminate all disturbances such as surrounding reflections, local

variations of surface optical absorption and infrared emission coefficient,

and inhomogeneous illumination by heating sources.21 Choi evaluated

the sizes and locations of subsurface defects by using LIT and showed

that a phase difference between the defective area and the healthy area

indicates the qualitative location and size of the defect.22

In LIT, energy is delivered to the specimen’s surface in the form of

periodic thermal waves and thermograms (Thermal Images) are captured

under the periodic sinusoidal heating. The excitation frequency is chosen

based on the diffusion length of a thermal signal and the images are

extracted.23-25 The size of defects can be measured directly from the

thermal images by exactly knowing the spatial resolution of the employed

optic.17 However, detailed information about defect parameters such as

size, depth and thermal resistance can be obtained by applying post-

processing procedures to the thermograms. LIT typically use amplitude

and phase measurements for the assessment of underlying defects. The

phase and amplitude information calculated by processing of recorded

thermal images for each of the pixels are stored in the form of 2D

matrices and subsequently converted to images known as phase image

and amplitude image.26-29 The amplitude image displays total temperature

increase on the system during power cycling and phase image represents

the time delay between powering a device and subsequent heating on

the surface. Amplitude images are quantitatively analyzed to acquire

the defect shape & size and phase image for defect’s depth.

Finite Element Analysis (FEA) is used to predict the experimental

results since the experiments are difficult to implement and they are

costly. Modeling of IRT helps to obtain the physical insight of the

thermal phenomena occurring during and after thermal excitation of

structures and helps fully understand all the aspects of their thermal

behavior. FEA is widely adopted by many industries and researchers for

modeling and simulation because it is faster and cheaper than physical

testing. However, the test validation is generally compulsory.30-32

This paper presents the experimental investigation and FEA model

to simulate the thermal phenomena in IRT for evaluation of subsurface

defect size and depth. A reference STS 304 specimen with known

artificial defects of flat bottomed holes of different size and depth was

used for the analysis. Amplitude and phase images were acquired by

post processing of thermal images using LIT. Finally, the data was

processed in MATLAB and LABVIEW for the measurement of defect

size and depth.

2. Principle of Lock-in Infrared Thermography

The periodical transfer of heat at the surface of a homogeneous

semi-infinite material results in a thermal wave, which in one dimension

is given by,6,9,29

(1)

where, T0 [°C] is initial change in temperature produced by the heat

source, ω [rad/s] is the modulation frequency, A(z) is the thermal

amplitude, is the phase, f [Hz] is the frequency, λ [m] is thermal

wavelength, z [mm] is the defect depth, and µ [m] is thermal diffusion

length which determines the rate of decay of thermal wave as it

penetrates through a material and defined by,4,6,10

(2)

From the Eq. (1), we can get the phase difference (Φ), which is

related to the defect depth as,24

(3)

In lock in thermography, after externally heating the specimen

sinusoidally, the resultant temperature distribution on the surface is

observed in the stationary regime and the corresponding data is recorded

in real time. The four point method is used to determine the phase and

amplitude data. If S1, S2, S3 and S4 are four equidistant temperature data

points as shown in Fig. 1 in a complete period then the phase ( ) and

amplitude (A) are given by,3,10

(4)

(5)

Tz t,

T0

z

µ---–⎝ ⎠

⎛ ⎞ i ωtz

µ---–⎝ ⎠

⎛ ⎞expexp A z( ) i ωt ∅ z( )–[ ]exp= =

∅ z( )

µ2α

ω-------

α

π f-----= =

∅z

µ---=

∅

∅S1

S3

–

S2

S4

–--------------⎝ ⎠⎛ ⎞1–

tan=

A S1

S3

–( )2 S2

S4

–( )2+=

Fig. 1 Principle of computation of thermal, amplitude and phase

images in lock-in thermography

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 16, No. 11 OCTOBER 2015 / 2257

3. Materials and Methods

3.1 Experimental approach

In the present investigation, a square shaped (180 m * 180 mm) STS

304 specimen with 10 mm thickness and artificial defects with circular

cutouts of varying depth and diameter at the back side was manufactured.

The idealized shape of a circular defect was chosen to illustrate the

effects of geometry on the observed thermal response. Each column of

cutouts in the STS plate represents defects of constant diameter but of

varying depth from upper row to lower row. The Fig. 2 shows the detail

of the geometry of the specimen with artificial defects and Table 1

shows the detail of the defects size and depth considered in the

investigation.

The success of LIT technique eventually depends on the quality of

the raw thermograms. Good thermograms are obtained when the test

specimen is a perfect blackbody. The sound side of the plate was

painted with KRYLON flat black paint which has an emissivity of

approximately 0.92. The specimen was painted black to increase the

surface emissivity of the specimen and provide a uniformly emissive

surface. The Fig. 3(a) shows the front side of the specimen with black

paint and the Fig. 3(b) shows the rear side with artificial defects.

Experimental tests were performed using excited LIT in the

reflection mode. A sinusoidal thermal source was used to excite the

surface of a specimen. The thermal excitation source consisted of two

halogen lamp of 1 kW each, driven by power amplifier and a function

generator. A programmable function generator (Agilent 33210A,

Malaysia) was used for the generation of sine waves. For detection of

thermal waves, infrared camera (SC645, FLIR Systems, Sweden) was

used which has a 640*480 pixel resolution and sensitivity of 7.5-13 µm.

An IR lens of 41.3 mm with spatial resolution 0.41 mrad and dynamic

range of 14 bit was used with the camera, positioned to fully utilize the

field of view encompassing the entire specimen. Images were acquired

by using FLIR R&D software. The camera frame rate was set to 50

frames per second with acquisition times for each frequency. The Fig.

4 shows the schematic of experimental LIT testing device configuration.

For a correct non-destructive evaluation, it is necessary to know the

thermal diffusivity, thickness of the object under inspection and

appropriate selection of wave frequencies. One of the main drawbacks

of LIT is that blind frequencies affect the defect detection. If a wrong

excitation frequency is chosen, a defect might be loss. To address this

shortcoming, lock-in test were performed at multiple frequencies.

Practically, any frequency in the range from 0.01 to 0.09 Hz can be

used for lock in thermography.15 For the accurate selection of excitation

frequency range, the diffusion length for the defect depth of 5, 6, 7 and

8 mm were calculated in terms of amplitude and phase image. During

the calculation, the depth range of µ and 1.8 µ were considered for the

Fig. 2 Geometry of specimen with artificial defects

Table 1 Details of defects size and depth

Hole Number Diameter(mm) Depth (mm)

A1 16 8

A2 4 8

A3 8 8

A4 12 8

B1 16 5

B2 4 5

B3 8 5

B4 12 5

C1 16 7

C2 4 7

C3 8 7

C4 12 7

D1 16 6

D2 4 6

D3 8 6

D4 12 6

Fig. 3 STS Model Specimen, (a) Front Side with black paint, and (b)

Rear Side with flat bottomed holes

Fig. 4 Schematic of LIT testing device configuration


amplitude image and phase image respectively. The Table 2 shows the

calculated diffusion length and the range of excitation frequency for the

different defect depth in STS specimen. From the Table 2, it is known

that a thermal wave can penetrate deeper at low frequencies.

3.2 Finite Element Analysis (FEA)

To simulate the thermographic inspection, 3D heat flow simulation

model has been developed by using a commercial finite element

modeling computer package ‘ANSYS Version 14.0’. In FEA modeling,

the same geometry and the artificial defects were considered which were

used in the experiment. The Fig. 5(a) shows the front side of FEA

model and the Fig. 5(b) shows the rear side of FEA model with artificial

defects. During meshing, a tetrahedral meshing was adapted to the

various domains of the sample. Physical preference was taken as

mechanical with relevance 100, relative center was kept in fine mode,

proximity and curvature was on in advanced size function in order to

calculate temperature variations with sufficient spatial resolution. The

resultant mesh had 655,015 elements and 940,245 nodes. The finite

element model of the specimen with meshing is shown in Fig. 5(c). The

thermal analysis was considered for recording the temperature time

histories to determine the phase difference associated with the frequency

of the sinusoidal wave form. The thermal properties and geometrical

parameters considered for FEA-model are shown in the Table 3.

In this model, the influence of radiative and convective heat transfer

is neglected. The boundary condition for the heat flux can be written

in the following form,4,32

(6)

where, is the term which describes the heat flux on the irradiated

surface.

The ambient temperature Tamb

measured in the room was used both

as boundary condition and initial condition since it was assumed that

the specimen was in equilibrium with the environment at room

temperature before the experiment started. The initial condition is,

(7)

The front surface of the plate is subjected to plane harmonic heat.

The equation, which represents the heat source and generate a continuous

sinusoidal wave is given by,11,23

(8)

where, Q0 is the intensity of heat source, ω is the angular modulation

frequency and t is the time.

Once the thermal sinusoidal wave hits the material, its response also

k.∇T( ) ∅0

=

∅0

T x y z t = 0, , ,( ) Tamb

24°C= =

qQ

0

2------ 1 ω t( )cos+( )=

Table 2 Theoretically calculated diffusion length and excitation

frequency for different defects depth

Defect

Depth

(mm)

Amplitude Image Phase Image

Diffusion

length (mm)

Frequency

(Hz)

Diffusion

length (mm)

Frequency

(Hz)

8 8.07 0.021 4.39 0.071

7 6.99 0.028 3.84 0.093

6 6.00 0.038 3.30 0.126

5 4.99 0.055 2.74 0.182

Fig. 5 FEA Model, (a) Front Side, (b) Rear Side with flat bottom

holes, and (c) Meshing description

Table 3 Geometrical parameters and thermal properties of STS 304

Length (X) 0.18 m

Length (Y) 0.18 m

Length (Z) 0.01 m

Mass (m) 2.4822 kg

Volume (v) 3.142E-4 m3

Thermal Conductivity (k) 16.2 W/m/oC

Specific Heat (C) 477 J/kg/oC

Density (ρ) 7900 kg/m3

Initial Temperature 24 oC

Nodes 940,245 No.

Elements 655,015 No.


becomes sinusoidal in nature with both phase and amplitude. The Fig.

6(a) and 6(b) shows the nature of excitation heat flux by a time varying

sinusoidal wave for the frequency of 0.182 Hz and 0.021 Hz respectively.

The temperature of all the nodes is computed considering the thermal

properties of the material and the thermal exchanges between the nodes

and with the external environment. The thermal data were then analyzed

by applying an image processing algorithm to these temporal series of

images representing the evolution of the temperature at the excited

surface.

4. Results and Discussion

The experimental analysis and the finite element analysis was carried

out for the 4 complete excitation cycles at the frequency ranging from

0.182 Hz (21.96 sec) down to 0.021Hz (190.48 sec). During heat

excitation thermal images represented with x, y coordinates are captured

by IR camera. The sequence of thermal images is monitored and recorded

in time. Some examples of thermal, amplitude and phase images

obtained from both the experimental and finite element analysis at

different frequencies will be discussed in this section.

In the thermal image, each pixel represents a temperature value. For

the comparison of surface temperature distribution between varying

defects diameter and depth, the measurement profile A-A’ and C-C’

were created along the defects A1, A2, A3 & A4 with defect depth of 8

mm and along the defects C1, C2, C3 & C4 with defect depth of 7 mm

as shown in Fig. 7(a). The thermal image at frequency 0.071 Hz and

time 21.13 second was considered for the analysis. From the Fig. 7(b)

and 7(c), it is observed that high heat flow value gives a greater

temperature difference in the defects surface and is favorable for the

Fig. 6 Input heat flux with time for, (a) 0.182 Hz (b) 0.021 Hz

Fig. 7 Temperature Distribution, (a) Thermal Image at frequency 0.071

Hz and time 21.13 sec (b) Temperature distribution profile along the

line A-A’ and (c) Temperature distribution profile along the line C-C’

Table 4 Time calculation for the selection of images

Frequency

(HZ)

Time (Sec) Image (with respect to time)

Cycle

time

Image

interval1st 2nd 3rd 4th

0.182 5.49 1.37 8.24 9.62 10.99 12.36

0.126 7.94 1.98 11.90 13.89 15.87 17.86

0.093 10.75 2.69 16.13 18.82 21.51 24.19

0.071 14.08 3.52 21.13 24.65 28.17 31.69

0.055 18.18 4.55 27.27 31.82 36.36 40.91

0.038 26.32 6.58 39.47 46.05 52.63 59.21

0.028 35.71 8.93 53.57 62.50 71.43 80.36

0.021 47.62 11.90 71.43 83.33 95.24 107.14


defect detection. The thermal contrast depends on the variation of the

defect depth and increases with the defect depth. From the comparison

of experimental and FEA results, it was also found that the temperatures

obtained experimentally are higher than those obtained from FEA

although they have similar trends.

The thermal images were selected from the 2nd cycle for each

excitation frequencies and then post-processed to determine the phase

and amplitude of the periodic temperature change at the specimen

Fig. 8 Thermal, Amplitude and Phase Image at frequency 0.182 Hz,

(a) Experimental Analysis and (b) FEA




surface. The four point method was used in post processing every pixel

of thermal images using the MATLAB programming language. The

Table 4 shows the list of selected thermal images with respect to time

at different frequencies for post processing.

The thermal, amplitude and phase images obtained by LIT from

experimental investigation and FEA at 0.182 Hz, 0.071 Hz and 0.021

Hz using the Eqs. (4) and (5) are shown in Figs. 8, 9 and 10. As per

the experimental results, it is observed that no defects were detected at

the highest frequency 0.182 Hz. As the frequency decreased to 0.126

Hz, the contrast begins to improve and when it goes down to 0.021 Hz,

the surface defects in phase and amplitude become more visible. It is

observed that information about deeper feature was available when

lower frequencies were used although testing time become quite long.

But in case of finite element analysis, the defects were visible in all the

cases of excitation frequency however as the frequency goes down the

thermal contrast increases which tend to increase the noise in the

images. It is also observed that the maximum phase information was

obtained at high frequency 0.182 Hz and the maximum amplitude

information at low frequency 0.021 Hz. Compared to experimental

results, the finite element analysis has higher capability to detect the

size of subsurface defects in an amplitude image due to the larger

temperature difference between defective regions and non-defective

one.

Signal to Noise Ratio (SNR) is the decisive quantity that determines

how small a defect or any other feature can be detectable. The SNR is

the ratio of the strength of the signal and the strength of the noise. SNR

describes the contrast between a defective area and its neighborhood.33

SNR analysis was performed on both phase and amplitude images in

each excitation frequency to determine the optimum frequency at which

the highest signal to noise ratio was recorded. For this purpose, two



Fig. 11 SNR of Phase and Amplitude Image as a function of excitation

Frequency, (a) Experimental Analysis and (b) FEA


areas for each defect are selected; an area in the defect that will be

considered as ‘signal’ and an area around the defect defined as ‘Noise’.

From the Fig. 11, it is observed that with increment in excitation

frequency, the SNR of phase image increases while at the same time the

SNR of amplitude image goes down in both experimental investigation

and FEA. So 0.071 Hz was selected as a blind frequency where SNR

is maximum and the maximum numbers of defects are visible for both

amplitude and phase image. Although the SNR is high and maximum

number of defects is visible in the range of 0.071 Hz down to 0.055 Hz

in both FEA and experimental analysis, the information about the

deeper defects is not effective. So the optimum frequency of 0.021 Hz

was considered for the analysis where phase difference between the

sound area and defective area was found minimum.

Phase difference was evaluated by subtracting the phase value

located centrally over the defects from the phase value measured in the

sound area near the defects. Analysis of phase image was performed in

relation to excitation frequency and defect depth. The Fig. 12 shows the

plot of defect’s phase as a function of excitation frequency. The defect

A1 with larger diameter 16 mm and deeper depth 8 mm is chosen for

the analysis. It is observed that with increase in excitation frequency,

the phase difference between the sound area and the defective area

increases for the same defect size and depth. The Fig. 13 shows the

phase measured over the defects as a function of defect depth for a

thermal excitation frequency of 0.021 Hz. Again the same defect A1

with larger diameter 16 mm and varying depth of 5 mm, 6 mm, 7mm

and 8 mm is chosen for the analysis. It was found that, the phase

difference increases with the defect’s depth.

The size of defect was evaluated by considering the amplitude image

which displayed the better contrast. For this, Vision Assistant, a special

tool in LABVIEW Program was used. By default, Vision assistant returns

measurement in pixel units. For the inspection to return measurement

in the real world units, mapping of pixel units in real world units was

done through a process called spatial calibration. Phase image was used

for calculating the defect’s depth. The variation in the high and low

phase values produce by the defect was exploited to evaluate the depth

of defects. The depth of a defect was calculated by using Eq. (3) and

considering the thermal diffusivity of the material and the heating

frequency for which defects become visible. The Table 5 shows the

results obtained from experimental and FEA data quantification at the

optimum frequency of 0.021 Hz.

Finally, it is observed that, the defects A1 (Diameter Size 16 mm &

Defect Depth 8 mm), A4 (Diameter Size 12 mm & depth 8 mm), C1

(Diameter Size 16 mm & depth 7 mm) and C4 (Diameter Size 12 mm

& depth 8 mm) are detachable and measurable in both the FEA and

experimental analysis. The defects A2, B2, C2 & D2 with the minimum

diameter size 4 mm and depths 8, 5, 7 and 6 mm respectively were not

detachable in both the FEA and experimental analysis. As compared to

Fig. 12 Plot of phase difference Vs. thermal excitation frequency

Fig. 13 Phase value for 16 mm diameter as a function of defect depth

Table 5 Estimated defect size and depth

HoleActual Experimental FEA

Diameter Depth Diameter Depth Diameter Depth

A1 16 8 16.20 7.95 16.10 8.28

A2 4 8 - - - -

A3 8 8 - - 8.05 8.55

A4 12 8 11.71 6.16 12.20 8.32

B1 16 5 - - 16.59 5.55

B2 4 5 - - - -

B3 8 5 - - - -

B4 12 5 - - - -

C1 16 7 14.59 7.08 14.15 8.79

C2 4 7 - - - -

C3 8 7 - - 8.29 7.68

C4 12 7 9.95 6.06 12.44 7.26

D1 16 6 - - 16.1 6.47

D2 4 6 - - - -

D3 8 6 - - - -

D4 12 6 - - 11.71 6.70

Fig. 14 Error % as a function of defect size


experimental results, the defects; A3 (Diameter size 8 mm and depth 8

mm); B1 (Diameter size 16 mm and depth 5 mm); C3 (Diameter size

8 mm and depth 7 mm); D1 (Diameter 16 mm and depth 6 mm) and

D4 (Diameter 12 mm and depth 6 mm) are more clearly visible and

measureable although the optimum frequency for each defect is

different measurement accuracy is low. The Figs. 14 and 15 shows the

errors present in quantification of defects which were noticeable in both

experimental investigation and FEA. From the both Figs. 14 and 15, it

is found that due to change in the defect size the error percentage varies

for the same defect depth. It is also observed that the defects with

radius to depth ratio 1 or greater than 1 are found to be detectable with

high accuracy.

5. Conclusion

This study explored the use of LIT and image processing algorithms

for quantitative assessment of sub-surface defects in STS 304 material.

The results shows, infrared thermography is a reliable non-destructive

method for detecting the defects size and depths and the detachability

improves as the defect radius to defect depth ratio approximates unity.

A finite element analysis was found to have good correlation with

experimental data and thus demonstrate potential in providing improved

estimates of defect depth.

The detachability of subsurface defects by LIT depends on material

properties, defect size and depth, geometry and surface finish of the

component, IR Camera thermal sensitivity, excitation frequency, heating

power etc.

So it is realized that the development of algorithm by considering the

defect size will help to improve the efficiency of LIT for the calculation

of the defects depth. Furthermore, comparing the experimental results

with the FEA, the visibility of defects in experiment is limited by

structural and apparatus noise. The structural noise depends on the

material characteristics and on the boundary conditions and is difficult

is to eliminate. The detector noise can be reduced by using modern

FPA cameras with high sensitivity and spatial resolution.

ACKNOWLEDGEMENT

This work was supported by the National Research Foundation of

Korea (NRF) grant funded by the Korea government (NRF-2010-0023

353) and, by the Human Resources Development program (No. 201540

30200940) of the Korea Institute of Energy Technology Evaluation and

Planning (KETEP) grant funded by the Korea government Ministry of

Trade, Industry and Energy.

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