Quantitative Pulsed Phase Thermography (PPT) has been only used to estimate defect parameters such as depth and thermal resistance. Here, we propose a thermal quadrupole based method that extends quantitative pulsed phase thermography. This approach estimates thermal diffusivity by solving a inversion problem based on non-linear squares estimation. This approach is tested with pulsed thermography data acquired from a composite sample. We compare our results with another technique established in time domain. The proposed quantitative analysis with PPT provides estimates of thermal diffusivity close to those obtained with the time domain approach. This estimation requires only the a priori knowledge of sample thickness.
Infrared (IR) images are representations of the world and have natural features like images in the visible spectrum.
As such, natural features from infrared images support image quality assessment (IQA).1
In this work, we
compare the quality of a set of indoor and outdoor IR images reconstructed from measurement functions formed
by linear combination of their pixels. The reconstruction methods are: linear discrete cosine transform (DCT)
acquisition, DCT augmented with total variation minimization, and compressive sensing scheme. Peak Signal to
Noise Ratio (PSNR), three full-reference (FR), and four no-reference (NR) IQA measures compute the qualities
of each reconstruction: multi-scale structural similarity (MSSIM), visual information fidelity (VIF), information
fidelity criterion (IFC), sharpness identification based on local phase coherence (LPC-SI), blind/referenceless
image spatial quality evaluator (BRISQUE), naturalness image quality evaluator (NIQE) and gradient singular
value decomposition (GSVD), respectively. Each measure is compared to human scores that were obtained by
differential mean opinion score (DMOS) test. We observe that GSVD has the highest correlation coefficients of
all NR measures, but all FR have better performance. We use MSSIM to compare the reconstruction methods
and we find that CS scheme produces a good-quality IR image, using only 30000 random sub-samples and 1000
DCT coefficients (2%). In contrast, linear DCT provides higher correlation coefficients than CS scheme by using
all the pixels of the image and 31000 DCT (47%) coefficients.
Disorders associated with repeated trauma account for about 60% of all occupational illnesses, Carpal Tunnel Syndrome (CTS) being the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine. IT is non-invasive and detects diseases based on measuring temperature variations. IT represents a possible alternative to prevalent methods for diagnosis of CTS (i.e. nerve conduction studies and electromiography). This work presents a set of spatial-temporal features extracted from thermal images taken in healthy and ill patients. Support Vector Machine (SVM) classifiers test this feature space with Leave One Out (LOO) validation error. The results of the proposed approach show linear separability and lower validation errors when compared to features used in previous works that do not account for temperature spatial variability.
Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection.
It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those
flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest
(ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology
for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform
heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in
In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is
robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian
scale analysis and local edge detection. In this methodology local correlation between image and Gaussian
window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well
modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using
multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size.
Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide
a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the
other dedicate algorithms proposed in the state of art.
The modified DAC version with thermal quadrupoles can be considered an interesting alternative to thermal
contrast computations since it provides an automated tool for depth retrieval and eliminates the need of selecting
a non-defective area. In practice it is important to have heat stimulus with complex shapes and long durations
(several seconds) in order to cover larger inspection areas, enhance thermal contrast between defective and sound
areas and increase the depth of inspection inside the material. In this work we present a heat stimulus correction
by using the thermal quadrupoles theory and its validation with several heat stimulus shapes and durations.
Infrared thermography is a non-contact evaluation technique which allows not only the registration of the temperature distribution on a surface, but also the calculation of the amount of heat flowing through it. Boilers are important for industry and the quantification of the heat losses is beneficial to avoid fuel waste.
The present work suggests a methodology to calculate the thermic flow through boiler's isolation surfaces, using thermic images. With this, it is possible to find the flow by using a thermogram taking into consideration: the thermogram's range, knowing the camera's FOV, surface's emmisivity and characteristic length, object-to-camera distance, environmental temperature, and the assigned grey-level calibration curve to determined temperature range.
A software tool to upload and process the information was developed. This tool can calculate the surface's average convection coefficient hc by using empiric correlations developed for common geometries and heat transfer equations to calculate the thermic flow.
To test the technique functioning, the information given by the software tool was compared to the data given by the heat flow measurement thermal sensor. This comparison showed a 3% error range of relative error. The final validation was made on a waterwall-boiler's home isolated walls and the highest error obtained was close to 15%.
Regardless the calibration curve was found under laboratory conditions and the empiric correlations to calculate hc are for isometric surfaces, the methodology presented a good performance. This then is a first step to quantify the global heat losses on boiler's isolation surfaces.
The Infrared Nondestructive Testing (IRNT) methods based on thermal contrast are strongly affected by non-uniform heating at the surface. Hence, the results obtained from these methods considerably depend on the chosen reference point. One of these methods is Artificial Neural Networks (ANN) that uses thermal contrast curves as input data for training and test in order to detect and estimate defect depth.
The Differential Absolute Contrast (DAC) has been successfully used as an alternative thermal contrast to eliminate the need of a reference point by defining the thermal contrast with respect to an ideal sound area. The DAC technique has been proven effective to inspect materials at early times since it is based on the 1D solution of the Fourier equation. A modified DAC version using thermal quadrupoles explicitly includes the sample thickness in the solution, extending in this way the range of validity when the heat front approaches the sample rear face.
We propose to use ANN to detect and quantify defects in composite materials using data extracted from the modified DAC with thermal quadrupoles in order to decrease the non-uniform heating and plate shape impact on the inspection.