Chaire de recherche du Canada Titulaire : Xavier ?· Chaire de recherche du Canada Titulaire : Xavier…

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  • Xavier Maldague

    MaldagX@gel.ulaval.ca

    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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  • 2

    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.

  • 3

    1. Infrared vision1. Infrared vision

  • 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

  • Plancks Law

    5

    Plancks Law

    -2 .3 .8 3 5 8 14 400 400010

    -10

    10-5

    100

    105

    1010

    [m]

    N,

    b [W

    / m

    2

    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 m

    visible midIR

    longIR

    Wien

    displacement

    law

    1 m

    Lo

    i d

    e W

    ien

    2897/6000 ~ 0.5 m

  • 6

    Infrared

    thermograms

    Infrared

    thermograms

    Visible

    photographs

    Visible

    photographs

    Plancks Law

    Self-emissionSelf-emission

    Need

    illumination

    Need

    illumination

  • Non-thermal IR vision

    7

    NIR/SWIR

    reflections or

    transmissionsReflectography

    Transmittography

  • 8

    Thermal IR vision

    Thermal

    emissions

  • IR vision: summary of characteristics

    NIR (0.7-1.1 m) or

    SWIR(1.1-2.5 m)

    Reflections/transmissions

    Single images

    No advanced processing

    required

    Qualitative results

    MWIR (3-5 m) or

    LWIR (7-14 m)

    Thermal emissions

    3D thermogram sequences

    Advanced processing

    techniques

    Qualitative and quantitative

    9

    Non-thermal vision Thermal vision

  • 10

    2. Non-thermal Infrared NDT2. Non-thermal Infrared NDT

  • NIR reflectography/transmittography of GFRP

    11

    900 1700 nm

    NIR: reflection NIR: transmissionvisible images

    GFRP: Glass Fiber Reinforced Plastic

    front

    back

  • 12

    3. Thermal Infrared NDT: Passive Thermography3. Thermal Infrared NDT: Passive Thermography

  • 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 CCDD

    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

    R = 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

  • 19

    4. Thermal Infrared NDT: Active Thermography4. Thermal Infrared NDT: Active Thermography

  • 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 objects 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 objects surface condition and thickness;

    22

  • 23

    Non-uniform heating

    +

    =

  • 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 ln

    2

    1lnln

    =

    A=USVT

    )()()( tTtTtTaSd

    =

    ( ) nnN

    k

    Nnkjn tkTtF ImReexp

    1

    0

    )2( +==

    =

    ( )tTt

    ttTT ddac

    = )(

    012 =

    t

    TT

    ( )te

    QTtT

    +=

    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? Answ

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