Integrity Assessment of Pipelines and Industrial Assets by ... NDT by Infrared Thermography IR...
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Integrity Assessment of Pipelines and
Industrial Assets by Automated
Infrared Thermography (AIT)
Fernando LΓ³peza, Marc-Antoine Blanchet and Luc Mauzeroll
a Corresponding author: [email protected]
June 8, 2017
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Page 2 NDT in Canada 2017 Conference (June 6-8, 2017)
1. Introduction
2. NDT by Infrared Thermography
3. Registration and Advanced Processing of Thermographic Data a. The 2D Fourier Transform
b. The Phase Correlation Method
c. High Resolution Dynamic Thermography
4. Applications
5. Final Considerations
Outline
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1. Visual (Visual Inspection);
2. Penetrating Radiation (X-Rays and Neutron Imaging);
3. Magnetic (Magnetic Participles, Eddy Current);
4. Mechanical Vibrations (Ultrasound, Acoustic Emission);
5. Chemical (Chemical Spot Testing);
6. Infrared and Thermal (Infrared Thermography);
7. Optical (MoirΓ© Interferometry, Holography and Shearography);
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Introduction
Ch. Hellier. Handbook of Nondestructive Evaluation. McGraw-Hill, NY, 2003.
Non-destructive Testing Techniques
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1. Visual (Visual Inspection);
2. Penetrating Radiation (X-Rays and Neutron Imaging);
3. Magnetic (Magnetic Participles, Eddy Current);
4. Mechanical Vibrations (Ultrasound, Acoustic Emission);
5. Chemical (Chemical Spot Testing);
6. Infrared and Thermal (Infrared Thermography);
7. Optical (MoirΓ© Interferometry, Holography and Shearography);
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Introduction
Ch. Hellier. Handbook of Nondestructive Evaluation. McGraw-Hill, NY, 2003.
Non-destructive Testing Techniques
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Introduction
Integrity Assessments of Pipelines and Industrial Assets
Infrared Thermography (IRT) stands as one of the emerging NDT techniques aimed to improve the inspections processes and maintenance procedures, specially in complex situations where classic NDT methods present limitations (e.g., high temperature, lack of accessibility, real time results).
Most of the industrial components (pipelines, reservoirs, storage tanks, etc.) are affected by at least one heat transfer mechanism. This particularity makes IRT an attractive NDT approach, since it is based on the analysis of the thermal gradients produced by variations of the heat fluxes.
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1. Introduction
2. NDT by Infrared Thermography
3. Registration and Advanced Processing of Thermographic Data a. The 2D Fourier Transform
b. The Phase Correlation Method
c. High Resolution Dynamic Thermography
4. Applications
5. Final Considerations
Outline
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NDT by Infrared Thermography
IR thermal vision is the capability to detect and measure by artificial means, the IR radiation that all bodies with temperature above 0 K emit. IR vision is aided by computer sciences to process the acquired information.
Infrared thermography corresponds to the acquisition and analysis of IR thermal data related to the MWIR and LWIR regions of the electromagnetic spectrum.
Adapted from: F. LΓ³pez, PhD Thesis, Federal University of Santa Catarina/Laval University, 2014
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NDT by Infrared Thermography
Active approach: an external excitation is applied to provoke a heat flux within the object under study. Internal defects alter the heat flux producing measurable surface temperature patterns or thermal contrasts.
Requires deep knowledge of the physical phenomena during the tests, as well as parameters associated to IR equipment, surrounding and observed system.
Background
Thermal/optical Mechanical Electromagnetic
Defects
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NDT by Infrared Thermography
Passive approach: no external excitation is applied to produce a heat flows within the object of interest. There is enough thermal contrast between the background and features.
Carried out under normal operational conditions.
Background Features
Features of interests are normally at higher or lower temperature than the background
No external excitation is applied.
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1. Introduction
2. NDT by Infrared Thermography
3. Registration and Advanced Processing of Thermographic Data a. The 2D Fourier Transform
b. The Phase Correlation Method
c. High Resolution Dynamic Thermography
4. Applications
5. Final Considerations
Outline
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Image Representation using the Fourier Transform
The Fourier transform is a representation of an image as a sum of complex exponentials of varying magnitudes, frequencies, and phases. The Fourier transform plays a critical role in a broad range of image processing applications, including enhancement, analysis, restoration, and compression.
πΉ π1, π2 = π π, π πβππ1ππβππ2πβ
π=ββ
β
π=ββ
The Discrete Fourier Transform (DFT):
πΉ π, π = π π,π πβπ2πππ/π πβπ2πππ/ππβ1
π=0
πβ1
π=0
π π, π =1
ππ πΉ π, π πβπ2πππ/π πβπ2πππ/ππβ1
π=0
πβ1
π=0
π = 0, 1,β¦ ,π β 1
π = 0, 1,β¦ , π β 1
π = 0, 1,β¦ ,π β 1
π = 0, 1,β¦ ,π β 1
A DFT is a transform whose input and output values are discrete samples.
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Registration and Advanced Processing of Thermographic Data
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Image Representation using the Fourier Transform
Gray scale image Magnitude Spectrum Phase Spectrum
πΉ π, π = π π,π πβπ2πππ/ππβπ2πππ/ππβ1
π=0
πβ1
π=0
= π π + πΌπ
π΄ = π π2 + πΌπ2 π = π‘ππβ1πΌπ
π π
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Registration and Advanced Processing of Thermographic Data
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Phase (PC) Correlation Algorithm :
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Registration and Advanced Processing of Thermographic Data
The PC algorithm is based upon the that the information pertaining to the displacement of two images resides in the phase of the cross power spectrum.
π2 π₯, π¦ = π1 π₯ β π₯π, π¦ β π¦π
πΉ2 π’, π£ = πΉ1(π’, π£) πβπ2π π’π₯π+π£π¦π
* The Fourier magnitudes are the same, but their phase are different.
Let π1 (π₯, π¦) and π2 (π₯, π¦) be two image functions that differ by a displacement or translation of (π₯π, π¦π):
Their Fourier transform are related by:
π π’, π£ =πΉ1(π’,π£)πΉ2
β(π’,π£)
πΉ1(π’,π£)πΉ2β(π’,π£)= ππ2π π’π₯π+π£π¦π
The cross-power spectrum is given by:
* Phase difference between the Fourier Transform of both images.
π π, π =1
ππ π(π’, π£)π’,π£ ππ2π π’π₯π+π£π¦π = πΏ(π₯π, π¦π)
The inverse Fourier transform of the cross-power spectrum:
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Implementation of Phase (PC) Correlation Algorithm :
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Registration and Advanced Processing of Thermographic Data
Reference Image (MxN)
2D Fourier Transform Phase Extraction
(MxN)
Cross-power spectrum
2D Fourier Transform Phase Extraction
(MxN)
Input 2D Fourier Transform Phase Difference Output
Inverse of the cross-spectrum
ALLIGNED IMAGE
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High Resolution Dynamic Thermography- HRDT
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High Resolution Dynamic Thermography (HRDT) is an advanced processing methodology/environment developed by the R&D team of TORNGATS, which consists on the acquisition, processing and analysis of thermographic data in transient regime.
This methodology aims to enhance the detectability of subsurface defects in cases where important heat transfer losses take places.
The main premise used in this analysis is the fact that zones with internal anomalies will behave β thermally - different from the rest of the surface. By using advanced signal processing techniques, it is possible to increase the signal-to-noise ratio of thermal gradients produced by subsurface defects.
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High Resolution Dynamic Thermography- HRDT
Methodology:
Image Registration
Image Processing
Image Analysis
Image Acquisition
Image alignment based on phase correlation algorithm.
Dynamic acquisition and 3D matrix reconstruction
Factorial decomposition of 3D matrix.
Analysis of PLS loadings matrix and 3D sequence reconstruction
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High Resolution Dynamic Thermography- HRDT
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Objective: successive and simultaneous decomposition of X and Y into latent variables, describing maximum variance in X and maximum covariance in X in Y
Available algorithms:
β’ NIPALS (noniterative partial least squares); SIMPLS (available in MatLab)
π = ππβ² + πΈ π = ππβ² + πΉ
Predictors (Temperature data)
Predicted (Time series vector)
Residual Matrix
Scores (T,P): linear combination of the variables in X.
Loadings (P,Q): coefficients that define a linear combination of PLS
components
Multivariate statistical regression β Partial Least-Squares Regression:
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High Resolution Dynamic Thermography- HRDT
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3D Thermal Data 2D Raster-like Matrix X
N =NX β Ny
N = Nt
UNFOLDING
Nt
Ny
Nx
Row 1 Frame
Row 2 Frame
Row 3 Frame
Row Nt Frame
Row K Frame
= +X
Nt Γ Nx.Ny Nt Γ a
a Γ Nx.Ny
Nt Γ Nx.Ny
xT
P
E
Unfolded X matrix X-scores X-Loadings Errors
= +Y
Nt Γ 1 Nt Γ a
a Γ 1
Nt Γ 1
xT
Q
F
Y matrix y-scores y-Loadings Errors
= +X
Nt Γ Nx.Ny Nt Γ a
a Γ Nx.Ny
Nt Γ Nx.Ny
xT
P
E
Unfolded X matrix X-scores X-Loadings Errors
= +Y
Nt Γ 1 Nt Γ a
a Γ 1
Nt Γ 1
xT
Q
F
Y matrix y-scores y-Loadings Errors
π = ππβ² + πΈ
π = ππβ² + πΉ
Data Structure:
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1. Introduction
2. NDT by Infrared Thermography
3. Registration and Advanced Processing of Thermographic Data a. The 2D Fourier Transform
b. The Phase Correlation Method
c. High Resolution Dynamic Thermography
4. Applications
5. Final Considerations
Outline
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Inspection of a pipe with 1 ΒΎ diameter with isolation:
Applications: Detection of corrosion under isolation (CUI)
Visible image Single thermal image
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-0.015
-0.01
-0.005
0
0.005
0.01
80
100
120
140
160
180
60
80
100
120
140
160
180
200
Aligned image at 4m53s Aligned image at 2m08s 2nd PLS Component
Applications: Detection of corrosion under isolation (CUI)
Inspection of a pipe with 1 ΒΎ diameter with isolation: Internal corrosion confirmed with X-Ray
RAW IMAGES PROCESSED BY HRDT
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Applications: Characterization of the level of internal deposits
Inspection of a pipe with 20 inches diameter without isolation:
Visible image Single thermal image
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-0.015
-0.01
-0.005
0
0.005
0.01
50
100
150
200
250
0
50
100
150
200
Applications: Characterization of the level of internal deposits
Inspection of a pipe with 20 inches diameter without isolation:
Aligned image at 5m38s Aligned image at 2m06s 2nd PLS Component
RAW IMAGES PROCESSED BY HRDT
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Applications: Inspection of rooftops by Aerial Infrared Thermography
β’ Project EGP 506183 β 16. Aerial infrared thermography (AIT): Methodology to calculate energetic parameters of building rooftops based on direct temperature measurement.
β’ Financial Institution: Natural Sciences and Engineering Research Council of Canada β’ Partner: UniversitΓ© Laval (Professor Xavier Maldague)
5635 Rue Rideau, Ville de QuΓ©bec, QC G2E 5V9
Hexacopter model DJI S800 EVO
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Applications: Inspection of rooftops by Aerial Infrared Thermography
β’ Project EGP 506183 β 16. Aerial infrared thermography (AIT): Methodology to calculate energetic parameters of building rooftops based on direct temperature measurement.
β’ Financial Institution: Natural Sciences and Engineering Research Council of Canada β’ Partner: UniversitΓ© Laval (Professor Xavier Maldague)
5635 Rue Rideau, Ville de QuΓ©bec, QC G2E 5V9
80 m
122 m
Tacq = 10 min Tacq = 10 min
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Applications: Inspection of rooftops by Aerial Infrared Thermography
T (Temperature) vs. t (time)
0 10 20 30 40 500
100
200
v1.8 (dev)
T (Temperature) vs. t (time)
10 20 30 40 500
100
200
v1.8 (dev)
1st PLST Loading
Stabilized sequence
Raw sequence
x
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Final Considerations
Infrared thermography (IRT) is a promising technique for the non-destructive inspection of pipelines and industrial components. However, its accuracy and reliability greatly depend on the appropriate methodology of acquisition and post-analysis.
High Resolution Dynamic Thermography addresses the most important drawbacks of IRT when applied on complex situations: images misalignment and highly contaminated by noise (specially reflection and convection) and the anisotropy of the structures under investigation.
The R&D team of TORNGATS is permanently working on science-driven solutions for the most challenging situations in the industry. In this scenario, several R&D initiatives are in progress, aiming to provide to the industry unique, reliable and innovative NDT solutions.
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Acknowledgments
FrΓ©d Γric Lajoie TORNGATS - Gestionnaire des opΓ©rations par UAV