Analysis Of Thermographic Data Research Articles

The Potential of Infrared Thermography for Early Pregnancy Diagnosis in Nili-Ravi Buffaloes.

This study was designed to explore the potential of infrared thermography (IRT) as an alternate approach for early pregnancy diagnosis in buffaloes. The surface temperature (ST) of different regions (eyes, muzzle, flanks, and vulva) was determined in 27 buffaloes using IRT from the day of artificial insemination (AI; Day 0), and measurement was repeated every fourth day until Day 24 post-AI. From all regions, the ST in each thermograph was recorded at three temperature values (maximum, average, minimum). Pregnancy status was confirmed through ultrasonography on Day 30, and animals were retrospectively grouped as pregnant or non-pregnant for analysis of thermographic data. In pregnant buffaloes, all three values of ST were significantly greater (p ≤ 0.05) for the left flank, while, in the left eye and vulva, only the maximum and average values were significantly greater. By contrast, the maximum ST of the muzzle was significantly lower (p ≤ 0.05) in pregnant buffaloes compared to non-pregnant buffaloes. However, the ST of the right eye and right flank did not show significant temperature variation at any value. These findings suggest that IRT has the potential to identify thermal changes associated with pregnancy in buffaloes at an early stage.

A Physics-informed Neural Network for Pulsed Thermography- Based Defect Detection

The non-destructive testing technique of pulsed thermography has been widely used in materials to detect defects due to its low cost, large detection area, and rapid implementation. After conducting experiments, data processing is often necessary to reduce the influences of noise and non-uniform backgrounds and highlight defect information. However, during the analysis, physical information is usually ignored. In addition to thermographic data analysis, numerical simulations are also popular for analytical studies based on physical information, but they don’t fully utilize the experimental data. To address this, a new method was proposed using a physics-informed neural network (PINN) for thermographic data processing. PINN combines the prediction capabilities of deep neural networks with physical laws presented as partial differential equations and boundary conditions, allowing for both experimental data and physics information to be utilized in modelling. In pulsed thermography, the heat transfer is governed by Fourier's law of heat conduction in a three-dimensional system. However, there is a lack of temperature measurements in the depth direction. The proposed method solves this problem by using collocation points generated from Latin hypercube sampling. The PINN model provides a good estimation of the backgrounds in the thermograms, and the features of surface/subsurface defects are highlighted by subtracting the estimated backgrounds from the original thermograms. In the case study, the performance of the proposed method was found to be effective.

Data-Augmented Manifold Learning Thermography for Defect Detection and Evaluation of Polymer Composites.

Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials. However, the performance of these methods is still restricted by limited informative images and difficulties in feature extraction caused by inhomogeneous backgrounds and noise. In this work, a novel generative manifold learning thermography (GMLT) is proposed for defect detection and the evaluation of composites. Specifically, the spectral normalized generative adversarial networks serve as an image augmentation strategy to learn the thermal image distribution, thereby generating virtual images to enrich the dataset. Subsequently, the manifold learning method is employed for the unsupervised dimensionality reduction in all images. Finally, the partial least squares regression is presented to extract the explicit mapping of manifold learning for defect visualization. Moreover, probability density maps and quantitative metrics are proposed to evaluate and explain the obtained defect detection performance. Experimental results on carbon fiber-reinforced polymers demonstrate the superiorities of GMLT, compared with other methods.

TriMap thermography with convolutional autoencoder for enhanced defect detection of polymer composites

Pulsed thermography data are typically affected by noise and uneven backgrounds, thereby complicating defect identification. Hence, various image analysis methods have been applied to improve defect detectability. However, most of them directly analyze the original images, while the low quality of the data is disregarded. Herein, a thermographic data analysis method named TriMap thermography with convolutional autoencoder (CAE) is proposed to overcome this problem. In this method, a CAE is used to reduce noise and enhance the quality of thermograms. Subsequently, the TriMap algorithm is used to extract features from the enhanced data. Specifically, the TriMap uses triplet information to improve the low-dimensional embedding quality and obtain an abstract representation of high-dimensional data. Finally, defects and uneven backgrounds are effectively distinguished by visualizing the embedding vectors. The test results of a carbon fiber-reinforced polymer specimen validate the effectiveness of the proposed method.

Convolutional Graph Thermography for Subsurface Defect Detection in Polymer Composites

Infrared thermography for quality assessment of polymer composites has gained increasing attention with the development of various thermographic data analysis methods. However, low-quality thermal images containing noise and inhomogeneous backgrounds restrict the defect detection performance of these methods. In this work, a convolutional graph thermography (CGT) method is proposed for subsurface defect detection in polymer composites. With a developed convolutional autoencoder thermogram enhancer, the noise and inhomogeneous background of the thermal image can be reduced. Subsequently, a graph-based dimensionality reduction method performs feature extraction on the enhanced thermograms. Consequently, the low-dimensional intrinsic manifolds of the high-dimensional thermographic data are characterized to highlight the defects visually. Two case studies on carbon fiber reinforced polymers demonstrated the performance of the proposed method.

Exploratory factor analysis for defect identification with active thermography

Non-destructive testing (NDT) methods are commonly used to disclose defective inner structures of materials, where active infrared thermography is a popular NDT technique because of its easy operation, low cost, and the capability of rapid scanning of large areas. However, the thermographic signals often suffer from noise and non-uniform backgrounds, making defect identification difficult. Therefore, an additional processing step of thermographic data is often required to enhance the contrast between defects and their surroundings. In recent years, multivariate statistical analysis methods have been widely adopted to achieve this aim, among which principal component thermography (PCT) is a typical example. Sparse PCT (SPCT) further improves PCT by adding sparsity constraints to the optimization problem. Nevertheless, the performance of SPCT depends on the subjective selection of the tuning parameters. The optimal parameter values are case-dependent and unknown. In this work, an alternative thermographic data analysis method is proposed based on exploratory factor analysis (EFA). By means of factor rotation, EFA minimizes the complexity of factor loadings and makes the results more interpretable. In doing this, EFA provides results similar to those of SPCT, while there is no need for parameter selection. Experimental results illustrate the feasibility of the proposed method.

Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection.

Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.

Quaternion Processing Techniques for Color Synthesized NDT Thermography

Infrared thermography is a widely used technology that has been successfully applied to many and varied applications. These applications include the use as a non-destructive testing tool to assess the integrity state of materials. The current level of development of this application is high and its effectiveness is widely verified. There are application protocols and methodologies that have demonstrated a high capacity to extract relevant information from the captured thermal signals and guarantee the detection of anomalies in the inspected materials. However, there is still room for improvement in certain aspects, such as the increase of the detection capacity and the definition of a detailed characterization procedure of indications, that must be investigated further to reduce uncertainties and optimize this technology. In this work, an innovative thermographic data analysis methodology is proposed that extracts a greater amount of information from the recorded sequences by applying advanced processing techniques to the results. The extracted information is synthesized into three channels that may be represented through real color images and processed by quaternion algebra techniques to improve the detection level and facilitate the classification of defects. To validate the proposed methodology, synthetic data and actual experimental sequences have been analyzed. Seven different definitions of signal-to-noise ratio (SNR) have been used to assess the increment in the detection capacity, and a generalized application procedure has been proposed to extend their use to color images. The results verify the capacity of this methodology, showing significant increments in the SNR compared to conventional processing techniques in thermographic NDT.

Research on the effect of test parameters on signal contrast and accuracy of determining the size of a defect

Currently, is used several non-destructive testing methods for detecting defects in the composite materials. The most potential is methods non-contact scanning for a large surface area with relative insensitivity to environmental disturbances. In this regard, are developed methods for defect detection in composites, all of which have own peculiarity. A fundamental difference concerns the overall length of time is required for non-destructive testing and analysis of thermographic data from the infrared camera, first of all, for large-scale structure. This research work has been conducted a quantitative way of analyzing data to optimizing the testing parameters in terms of testing time and signal-to-noise ratio. In particular, were carried a few tests out on stringers of carbon fiber reinforced panels. These tests included lock-in thermography technique with simulated defects, which has been used for finding parameters to correct detect dimensions of defects.

Learning control coil currents from heat-flux images using convolutional neural networks at Wendelstein 7-X

An important goal of Wendelstein 7-X, the most advanced operating fusion experiment of the stellarator line, is to demonstrate the ability of stellarators to perform steady-state discharges. In this respect, the monitoring and control of the heat loads on the plasma facing components, especially of the strike-lines in the ten island divertors, will be critical during next operation phase OP2. In this paper, it is shown that deep convolutional neural networks are able to learn the relationship between the heat-flux images, obtained by the analysis of thermographic data, and the applied control coil currents in standard magnetic configuration experiments. This study is carried out in view of understanding and modeling the relationship between the heat-flux distribution in the divertor strike-lines and the actuators influencing them.

Multiview Learning for Subsurface Defect Detection in Composite Products: A Challenge on Thermographic Data Analysis

Nondestructive testing (NDT) is an economical way of detecting subsurface defects in composite products. Infrared thermography serves as a popular NDT method due to its high efficiency and low cost. However, defect identification by directly visualizing thermal images is difficult owing to the nonuniform background and noise. Recently, data analysis methods have been introduced to thermal image processing, including principal component analysis, which is known for its good performance in dimensionality reduction, feature extraction, and noise reduction. However, most of these methods can only extract linear features. In this article, a multiview learning-based autoencoder, which can process not only nonlinear features but also sequential attributes, is utilized in thermographic data analysis. After extracting the low-dimensional features by multiview learning, a background elimination step is conducted to highlight the locations and shapes of the defects. The experimental results demonstrate the feasibility of the proposed method.

Thermographic Data Analysis for Defect Detection by Imposing Spatial Connectivity and Sparsity Constraints in Principal Component Thermography

Data analysis methods have been extensively used in active thermography for defect identification. Among them, principal component thermography (PCT) is popular for dimensionality reduction and feature extraction. PCT summarizes the thermal images with a small number of empirical orthogonal functions that better reflect the information of defects. However, PCT does not induce sparsity, which limits the interpretation of PCT results. Recently, sparse PCT (SPCT) has been proposed to provide more interpretable analysis results. However, SPCT does not consider the spatial connectivity between pixels, omitting the fact that a defective region is usually spatially connected. In this article, a novel thermographic data analysis method is proposed to overcome the shortcomings of the existing methods. The proposed method imposes both spatial connectivity and sparsity constraints in PCT. Finally, one case study on an ancient marquetry sample and another on a carbon fiber-reinforced polymer composite illustrate the feasibility of the proposed method.

Defect Detection in Composite Products Based on Sparse Moving Window Principal Component Thermography

As a nondestructive testing (NDT) technology, pulsed thermography (PT) has been widely used in the defect detection of the composite products due to its efficiency and large detection range. To enhance the distinction between defective and defect-free region and eliminate the influence of the measurement noise and nonuniform background of the thermal image generated by PT, a number of thermographic data analysis approaches have been proposed. However, these traditional methods only consider the correlations among the pixel while leave the time series correlations unmodeled. In this paper, a sparse moving window principal component thermography (SMWPCT) method is proposed to incorporate several thermal images using the moving window strategy. Also, the sparse trick is used to provide clearer and more interpretable results because of the structure sparsity. The effectiveness of the method is verified by the defect detection experiment of carbon fiber-reinforced plastic specimens.

Generative Principal Component Thermography for Enhanced Defect Detection and Analysis

Machine learning methods play an important role in the nondestructive testing field for quality assessment of polymer composites. As a popular deep learning branch, a generative adversarial network is introduced to the thermography field as an image augmentation approach to improve its defect detection performance. Specifically, a generative principal component thermography (GPCT) method for defect detection in polymer composites is proposed. By employing the data augmentation strategy, more informative images are generated to enlarge the diversity of the original set of images. The defect detection results can be visualized using a number of interpretable features. Consequently, the defect detection performance of thermographic data analysis can be enhanced to some extent. The experimental results on a carbon fiber reinforced polymer specimen demonstrate the feasibility and advantages of the GPCT method.

Spatial-Neighborhood Manifold Learning for Nondestructive Testing of Defects in Polymer Composites

The subspace learning (dimensionality reduction) algorithms have played an important role in the analysis of thermographic data: a key step in infrared thermography-based nondestructive testing of subsurface defects in composite materials. However, one of its branches, manifold learning, with excellent ability to preserve local data structure, is rarely applied. In this article, a spatial-neighborhood manifold learning (SNML) framework is proposed for thermographic data analysis. Different from traditional manifold learning methods, SNML uses the spatial-neighborhood information instead of the traditional k-nearest neighbors, or ε-neighborhood, to construct the adjacency graph. This overcomes the difficulty of parameter selection and extracts local features in images in a more reasonable way. Additionally, the data preprocessing step and the means of thermographic data normalization in the proposed framework are discussed. For performance comparison, three traditional manifold learning methods are also implemented. The experiments on carbon fiber-reinforced polymer specimens demonstrate the validity and feasibility of SNML.

Non-destructive defect evaluation of polymer composites via thermographic data analysis: A manifold learning method

Recently, various thermographic data analysis methods have been utilized in the field of non-destructive evaluation (NDE) to process thermal images and enhance the visibility of defects. However, most of them extract only linear features, leading to cumbersome results. In this work, manifold learning is introduced into the thermographic data analysis field. As a nonlinear dimensionality reduction technique, manifold learning can identify an intrinsically low-dimensional manifold in a high-dimensional data space. Specifically, an isometric feature mapping (ISOMAP) based manifold learning thermography (MLT) method is proposed to analyze the thermographic data, which can effectively distinguish the uneven background, noise, and defect characteristics contained in thermal images and make the defect detection easier. The feasibility of MLT is illustrated using a carbon fiber-reinforced polymer (CFRP) specimen. The results show that, comparing to the conventional linear methods, the present method can better determine the defect information, including the positions, sizes, and shapes.

An investigation of the stepped thermography technique for defects evaluation in GFRP materials

The ability of thermography to detect defects in composite materials has been demonstrated and showed in various works and in many applications. In this regard, various NDT techniques are currently used for defect detection in composites such as Lock-in Thermography (LT), Pulsed (PT) Stepped Thermography (ST/SH), all of which have their own peculiarities and capabilities. A critical aspect concerns the overall lengthy time required for testing and analysis of thermographic data above all, for large structure where it is necessary a scanning approach. In this work, two algorithms based on the stepped thermography approach were investigated in quantitative way with the aim to optimizing the testing parameters and data analysis in terms of testing time and signal to noise ratio. In particular, several tests were carried out on a sample specimen with simulated defects and the well-established lock-in thermography technique has been used as comparison.

Sparse Principal Component Thermography for Subsurface Defect Detection in Composite Products

Active thermography is an efficient and powerful technique for nondestructive testing of products made of composite materials, which enables rapid inspection of large areas, presents results as easily interpreted high-resolution images, and is easy to operate. In recent years, a number of thermographic data analysis methods were developed to enhance the visibility of subsurface defects, among which principal component thermography (PCT) is recommended because of its capability to enhance the contrast between defective and defect-free areas, compress data, and reduce noise. In this study, a sparse principal component thermography (SPCT) method is proposed, which inherits the advantages of PCT and allows more flexibility by introducing a penalization term. Compared to PCT, SPCT provides more interpretable analysis results owing to its structure sparsity. The feasibility and effectiveness of the proposed method are illustrated by the experimental results of the subsurface defect characterization in a carbon fiber reinforced plastic specimen.

Quantitative analysis of thermographic data through different algorithms

Pulsed thermography is commonly used as non-destructive technique for evaluating defects within materials and components. However, raw thermal imaging data are usually not suitable for quantitative evaluation of defects. It was necessary to process the raw thermal data acquired to obtain a series of satisfactory results for a correct and quantitative material evaluation. In the last years, many data processing algorithms have been developed and each of them provide enhanced detection and sizing of flaws.In this work, starting from the same brief pulsed thermographic test carried out on an aluminium specimen with twenty flat bottom holes of known nominal size, different algorithms have been compared. The algorithms used have been: Pulsed Phase Thermography (PPT), Slope, Correlation Coefficient (R2), Thermal Signal Reconstruction (TSR), Principal Component Analysis (PCT). By analysing the results obtained using different approaches, it was possible to focus on the advantages, disadvantages and sensitivity of the various thermographic algorithms implemented.

Stable principal component pursuit-based thermographic data analysis for defect detection in polymer composites

Defects such as inclusions and voids are commonly observed in fiber reinforced polymer (FRP) composites. In order to ensure the quality of FRP products, it is desirable to have reliable and non-destructive testing techniques for detecting defects. Among existing techniques, pulsed thermography (PT) has the advantages of a wide scanning range and simple operation. However, thermal images generated using PT are often noisy, and can contain non-uniform backgrounds resulting from uneven heating. As a result, post-processing is necessary to improve the detection capability of PT. In this study, stable principal component pursuit (SPCP) is integrated with a moving-window strategy to decompose thermographic data into three parts: a low-rank matrix to approximately extract background information, a dense noise term containing most of the measurement noise, and a sparse matrix reflecting the defects in the tested specimen. In this manner, improved detection results can be obtained from the reconstructed thermal images based on the sparse matrix. The effectiveness of the proposed method is illustrated through experiments.