Principal Component Thermography Research Articles

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.

A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure

Electronic supply chain vulnerability has been threatened by counterfeits for decades and as a result, the authentication and quality of Electronic Components (ECs) are highly valued by stakeholders for safety-critical industrial systems. Thermography Nondestructive Testing (NDT), offering a rapid inspection while covering a large area within a short time frame, is a promising technique to inspect the integrity of ECs. However, recognising the internal structural layout directly through a packaged EC from thermography images is still a challenge due to its sealed and cramped structure. In this research, we propose a Pulsed Thermography (PT) based data analysis method for enhancement and identification of the layout of die and lead frame in ECs. The proposed solution combines the Sliding Window (SW) concept, Thermographic Signal Reconstruction (TSR) and Principal Component Thermographic (PCT) technique, named SWPCT, to enhance the contrast of die, die substrate and lead frame. Compared to the conventional full-scale PT image analysis, the proposed method presents its superior sensitivity in small thermal features by reducing the spatial thermal information redundancy benefiting from the sliding window PCT analysis. It overcomes the structural thermograms obstruction from the thick packaging, reveals the hidden die and lead frame layout and suppresses the noise simultaneously. By selecting a proper window size, this paper reveals that SWPCT can reconstruct the position and dimension of die and die substrate in ECs, and spatially distinguish the lead frame layout. Two groups of typical OpAmps chip samples with internal structure diversity (X-ray observed) were tested using PT to demonstrate the effectiveness of the proposed method. Simulation data were also used to verify this approach. This research could unlock the challenge of thermography-based inspection for key internal structure in the encapsulated integrated components.

Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study

Dimensional reduction methods have significantly improved the simplification of Pulsed Thermography (PT) data while improving the accuracy of the results. Such approaches reduce the quantity of data to analyze and improve the contrast of the main defects in the samples contributed to their popularity. Many works have been proposed in the literature mainly based on improving the Principal Component Thermography (PCT). Recently the Independent Component Analysis (ICA) has been a topic of attention. Many different approaches have been proposed in the literature to solve the ICA. In this paper, we investigated several recent ICA methods and evaluated their influence on PT data compared with the state-of-the-art methods. We conducted our evaluation on reference CFRP samples with known defects. We found that ICA outperform PCT for small and deep defects. For other defects ICA results are often not far from the results obtained by PCT. However, the frequency of acquisition and the ICA methods have a great influence on the results.

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.

Robust Principal Component Thermography for Defect Detection in Composites.

Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time.

Rectifying the emissivity variations problem caused by pigments in artworks inspected by infrared thermography: A simple, useful, effective, and optimized approach for the cultural heritage field

Inspection of cultural heritage objects plays nowadays paramount importance around the world. Non-destructive inspection is a must and a necessity in order to preserve the integrity of the artwork without losing any precious material composing it. The use of thermal non-destructive inspection is a good idea since, by exploiting the 3D diffusion inside the object, triggered by external radiation, surface and subsurface defects may be revealed. To do this, the long-wave infrared (LWIR) spectrum is usually exploited in combination with a thermal camera. In the cultural heritage field, the main problem to solve to detect as much as possible thermal imprints linked to invisible defects is the minimisation of the impact of emissivity variations caused by pigments composing the colours. A simple, effective, solid, and optimized method for decorated paintings is here applied right after some preliminary results. It is based on active thermography as the modality of inspection, and a thermal stimulus provoked by halogen lamps; thermal images recorded on a panel painting including man-made defects have been analysed and processed in MATLAB® environment. After extraction of de-nosing functions based on the heating and cooling steps, two methods are proposed to enhance the de-noised thermograms. Then, popular methods of Pulsed Phase Thermography (PPT) and the Principal Component Thermography (PCT) are applied. In the end, after minimizing the effect of emissivity variation, an optimization fusion proposal through post-processing the emissivity adjusted thermograms is provided. A brief but exhaustive review introduce and guide the readers towards the problem for which the authors took a step ahead via a proposal based on image fusion.

Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography.

Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method.

Active Thermography for Non-invasive Inspection of an Artwork on Poplar Panel: Novel Approach Using Principal Component Thermography and Absolute Thermal Contrast

The conservation of the works of art represent a topic of global interest. The development of effective tools based on advanced technology for analysing and monitoring their health-state is essential to assuring their preservation. In fact, detecting and preventing the formation of defective areas or assessing for an accurate pre-restoration analysis are the main objectives of non-destructive inspection. Active thermography is a well-known non-invasive imaging technique and reliable tool for providing a fast and low-cost analysis of a work of art. In this study we combine the potential of Principal Component Thermography and of Absolute Thermal Contrast to analyse thermal images acquired in-situ on a poplar panel painting representing an original artwork dating in the end of XVI century. We first optimized the thermal stimulation parameters in the laboratory using special phantom samples. These samples were specially made by reproducing in high fidelity the structural properties and materials of the artwork in order to perform effectively the preliminary tests. Then we moved the equipment in-situ by performing the non-destructive inspection directly on the real artwork. We have developed a specific experimental protocol that combines active thermography with two specific and appropriate image processing modalities that allowed us the effective detection of various types of defects in the painting layer. We report a complete analysis and deep discussion concerning the detection and characterization of the defects. Results show that our diagnostic protocol is a powerful tool in assessing the pre-restoration health-state and suitable for in situ analysis of wood artworks.

Constrained Autoencoder-Based Pulse Compressed Thermal Wave Imaging for Sub-Surface Defect Detection

Non-destructive testing & evaluation techniques play an essential role in ensuring safety of materials in operation at various industry sectors. Pulse compressed favourable thermal wave imaging is one of the widely used non-destructive testing techniques due to its excellent noise rejection capabilities. However, the high dimensional thermal imaging data needs to be encoded into lossless compressed form to highlight the hidden defects inside the materials. This paper proposes a novel constrained and regularized autoencoder based thermography approach for sub-surface defect detection in a mild steel specimen. Certain properties such as non-correlation of encoded data, weight orthogonality, and weights with unit norm length have been highlighted which are non-existent in linear autoencoders but are responsible for better defect detection inside the materials inspected by frequency modulated thermal wave imaging. Novel constraints are formulated for autoencoder cost function to incorporate these significant properties. The proposed approach is able to provide better defect detection, in terms of signal to noise ratio of defects, than linear autoencoder as well as traditional principal component thermography approach. Also, non-correlation of encoded data is found to be the most significant factor in achieving better defect detection followed by properties ensuring weight orthogonality and weights with unit norm length.

Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study

Active and passive thermography are two efficient techniques extensively used to measure heterogeneous thermal patterns leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex-, and sparse- non-negative matrix factorization (NMF) methods for detecting subsurface thermal patterns. These methods inherit the advantages of principal component thermography (PCT) and sparse PCT, whereas tackle negative bases in sparse PCT with non-negative constraints, and exhibit clustering property in processing data. The practicality and efficiency of these methods are demonstrated by the experimental results for subsurface defect detection in three specimens (for different depth and size defects) and preserving thermal heterogeneity for distinguishing breast abnormality in breast cancer screening dataset (accuracy of 74.1%, 75.8%, and 77.8%).

Defect Detection Capabilities of Pulse Compression Based Infrared Non-Destructive Testing and Evaluation

Infrared Thermography is one of the widely used method for non-destructive testing and evaluation methods due to its merits (remote, full field, safe and quantitative inspection capabilities) for testing and evaluation of wide variety of materials (metals, semiconductors and composites). Among the various thermal non-destructive evaluation modalities such as pulse based and mono frequency excited modulated lock-in thermography, recently proposed matched filter based non-periodic infrared thermographic approaches gained their importance due to superior test resolution and sensitivity for detection of defects hidden inside the test material. Further, feasibility to implement with low peak power heat sources in moderate experimentation time in comparison with conventional (pulse based thermographic techniques and in a limited span of time in comparison with mono frequency lock-in) thermographic techniques makes these pulse compression favorable techniques more economical and reliable. The present manuscript demonstrates the advantages of pulse compression favorable frequency modulated thermal wave imaging approach for identification of flat bottom holes in a glass fibre reinforced polymer material. The obtained results have been compared with widely used principal component thermography by taking the signal to noise ratio as a figure of merit.

Thermographic Inspection of Internal Defects in Steel Structures: Analysis of Signal Processing Techniques in Pulsed Thermography.

This study performed an experimental investigation on pulsed thermography to detect internal defects, the major degradation phenomena in several structures of the secondary systems in nuclear power plants as well as industrial pipelines. The material losses due to wall thinning were simulated by drilling flat-bottomed holes (FBH) on the steel plate. FBH of different sizes in varying depths were considered to evaluate the detection capability of the proposed technique. A short and high energy light pulse was deposited on a sample surface, and an infrared camera was used to analyze the effect of the applied heat flux. The three most established signal processing techniques of thermography, namely thermal signal reconstruction (TSR), pulsed phase thermography (PPT), and principal component thermography (PCT), have been applied to raw thermal images. Then, the performance of each technique was evaluated concerning enhanced defect detectability and signal to noise ratio (SNR). The results revealed that TSR enhanced the defect detectability, detecting the maximum number of defects, PPT provided the highest SNR, especially for the deeper defects, and PCT provided the highest SNR for the shallower defects.

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.

Principal Component Thermography for Defect Detection in Concrete.

The goal of the condition assessment of concrete structures is to gain an insight into current condition of concrete and the existence of defects, which decrease durability and usability of the structure. Defects are quite difficult to detect using infrared thermography when concrete elements cannot be thermally excited with the Sun, together with unfavorable thermophysical properties of concrete structures. In this paper, principal component thermography (PCT) is applied as a post-processing method to a sequence of thermograms in order to enhance defect detectability in concrete structures. Defects are detected by analyzing a set of empirical orthogonal functions (EOFs), which were acquired by applying principal component analysis to a sequence of thermograms. The research was performed using concrete samples containing known defects, which were tested using a step heating thermography setup. The results of presented research show that PCT is an effective post-processing method to improve defect detection in concrete structures. By effectively improving the defect detection, PCT has a potential to improve the non-destructive testing (NDT) accuracy of using infrared thermography (IRT) on concrete structures, especially in shaded areas of such structures. The research also shows the defect detectability depending on concrete type thermal excitation setup and defect geometry.

Physical insights into principal component thermography

Among widely used non-destructive testing (NDT) methods, infrared thermography (IRT) has gained importance due to its fast, whole-field, remote and quantitative inspection capabilities for the evaluation of various materials. Being fast and easy to implement, pulsed thermography (PT) plays a vital role in the infrared thermographic community. This paper provides a physical insight into the selection of empirical orthogonal functions obtained from principal component pulsed thermography for the detection of subsurface defects located inside a mild steel specimen.

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.

3D Defect detection using weighted principal component thermography

Quantitive infrared thermography like active thermography is a non-destructive testing technique that is used to inspect surfaces of components for defects. A problem with infrared-based defect detection is that misclassifications based on geometrically dependent measurement characteristics can occur. This problem becomes more problematic with the inspection of complex 3D shapes. In this paper, a new infrared quality control procedure using a quality map is proposed that models the geometrically dependent measurement characteristics based on available CAD data and CAD matching techniques. Misclassifications are reduced by using this quality map in combination with a modified version of principal component thermography post-processing. We applied our proposed methodology on a prototype bicycle part and a plaster cast angel figurine. In these experiments, the procedure using quality maps is able to prevent false defect detection.

Evaluating quality of marquetries by applying active IR thermography and advanced signal processing

Marquetry method is important in the culture of the Italian community as can be witnessed from the large quantity of artworks that have been realized in this way. The monitoring of the integrity of such pieces poses a great challenge given the need for a reliable and nondestructive technique able to detect surface and subsurface defects. In this work, two ancient marquetry samples containing natural defects were inspected thanks to active thermography by using time-tested, safe, and resilient advanced signal processing algorithms (i.e., principal component thermography, correlation contrast, pulsed phase Fourier transform amplitude and phase, cold image subtraction contrast, and polynomial fitting). The latter have been applied to provide a 2D map of the defects. Anyway, in the cultural heritage field, one of the main interests of restorers is the volume of the subsurface defects for structural analyses. The emphasis in this study is placed on the use of dynamic thermal tomography (DTT) as an advanced technique of active thermal nondestructive testing. The main concepts of DTT are illustrated in the manuscript, while a special technique for defect thermal characterization has been used during the second analysis to validate tomographic results. Finally, the position of the main defects retrieved by means of the established techniques applied during the first analysis has been confirmed by DTT.

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.

Boron nitride inclusions within adhesive joints: Optimization of mechanical strength and bonded defects detection

Non Destructive Testing (NDT) by active InfraRed Thermography (IRT) of bonded Carbon Fibers Reinforced Plastic (CFRP) laminates is a very challenging issue. Adding Boron Nitride (BN) particles within the joint material provides an interesting solution to better capture bonding defects. Though increasing additives leads to the improvement of NDT, the validation of this solution requires the mechanical characterization of the new joint material. In this way, the influence of particles on the mechanical properties of the adhesive joint is first investigated via double strap lap shear tests for different BN volume fraction. At the same time, controlled size void inclusions are generated within composite assemblies in order to evaluate the IRT ability to detect bonded defects inside BN-based adhesives. It is demonstrated that thermal data post-processing methods such as Singular Values Decomposition (SVD) and Principal Component Thermography (PCT) help to get the best trade-off between mechanical performance and defect detection.