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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.

Conservation of artworks is of paramount importance nowadays around the world. Clever conservation by using non-destructive testing techniques is highly appreciated by restorers and art historians. Among these, active infrared thermography needs the use of numerical simulations concerning the heat transfer from the heat source into the target. This, in order to gently heat the precious surface by avoiding any type of damage, such as colour changes and/or shrinkage and warpage effects, therefore, the understanding of the nature of the pigments, as well as the mapping of the support and eventual foreign materials is of primary importance in the survey. It is possible to accomplish these tasks by applying, e.g. X-Ray Fluorescence spectroscopy and radiography (X-Ray), respectively. Here, the latter techniques have been used on an ancient marquetry sample. Consequently, a thermographic inspection was performed, while a thermal–numerical simulation was implemented in ANSYS® environment. Numerical simulations were able to visualize some types of defects by calibrating the thermal inspection based on a comparison among defective and sound areas. Results demonstrated how such an integrated method is useful to provide robust information without damage to the work of art under restoration.

This work is centred on an ancient Italian church. Since 2011, a restoration plan has been undertaken by following sequential phases. The methodological approach to restoration was guided by environmental monitoring campaigns. In particular, two thermo-hygrometric campaigns were carried out during the warm months of the years 2015 and 2016. The first set of measurements was executed during the restoration of facades and roofs, making it possible to reach even areas that are usually difficult to access. The second set was performed to evaluate the indoor thermo-hygrometric conditions following the work of the previous year. This was intended to assess their differences in variability, the influence of the outdoor environment and any real and perceived improvement. Results demonstrate that thermal images helped in identifying both the heat sources causing thermal discomforts and the good thermal capacity of masonries. Concerning the heat index (HI), the church showed an improvement in the trend of malaise perceived by people during the second summer period (∼2°C lower than 2015). Finally, in the last microclimate monitoring, the roof structure no longer acted as an amplifier for daily temperature excursions.

Online first; International audience; Conservation of artworks is of paramount importance nowadays around the world. Clever conservation by using non-destructive testing techniques is highly appreciated by restorers and art historians. Among these, a special set-up working into the infrared region is the flying spot laser thermography technique that uses a punctual stimulus to transfer the heat from the source into the artwork. In this way, the precious surface is gently heated by avoiding any type of damage, such as color changes and/or shrinkage and warpage effects. To the best of our knowledge, the flying spot laser thermography technique, in the configuration proposed, has been used here for the first time in the cultural heritage field. It was applied in combination with terahertz inspection, an emergent technique in the field of art research, completely non-invasive and safe for the operator. The target to be inspected was a panel painting executed by following the rules dictated by the art master Cennino Cennini. Results demonstrated how such an integrated approach is plausible to provide robust information concerning invisible defects to the naked eye.

In this paper, terahertz time-domain spectroscopy (THz-TDS) is used for the first time to detect fabricated defects in a glass fiber-skinned lightweight honeycomb composite panel. A novel amplitude polynomial regression (APR) algorithm is proposed as a preprocessing method. This method segments the amplitude–frequency curves to simulate the heating and the cooling monotonic behavior as in infrared thermography. Then, the method of empirical orthogonal function (EOF) imaging is applied on the APR preprocessed data as a postprocessing algorithm. Signal-to-noise ratio analysis is performed to verify the image improvement of the proposed APR-EOF modality from a quantitative point of view. Finally, the experimental results and the physical analysis show that THz is more suitable with respect to the detection of defects in glass fiber lightweight honeycomb composites.

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.

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.

The water ingress phenomenon in sandwich-structured composites used in the aerospace/aeronautical sector is a current issue. This type of defect can cause in the course of time several other defects at the boundary, such as corrosions, deformations, detachments. In fact, water may change its state of physical matter going towards the freeze–thaw cycle caused by the atmosphere re-entry of, e.g. space probes. In this work, the alveoli of a composite laminate have been filled with water, which was initially transformed into ice. By taking into account, the known quantity of water, the freeze–thaw cycle was simulated by Comsol Multiphysics® software, reproducing exactly the shape of the sandwich as well as the real conditions in which it was subsequently subjected in a climatic chamber. The experimental part consisted of monitoring the front side of the specimen by means of a thermal camera operating into the long-wave infrared spectrum, and by setting both the temperature and the relative humidity of the test chamber according to the values imposed during the numerical simulation step. It was found that the numerical and experimental temperature trends are in good agreement with each other since the model was built by following a physico-chemical point-of-view. It was also seen that the application of the independent component thermography (ICT) technique was able both to retrieve the positions of the defects (i.e. the water inclusions) and to characterize the defects in which a detachment (fabricated between the fibres and the resin) is present; the latter was realized above an inclusion caused by the water ingress. To the best of our knowledge, this is the first time that ICT is applied to satisfy this purpose.

Infrared thermography (IRT) is a competitive method for nondestructive testing, yet it is susceptible to errors when testing objects with complex geometries. This work investigates the effects of regulating different thermographic testing parameters to optimize the IRT outcomes when testing complex shaped geometries, particularly cylindrical coupons. These parameters include the scanning routine, feed-rate, and heat intensity. Fine-tuning these parameters will be performed with respect to three different variables consisting of workpiece density, defect size, and defect depth. The experimental work is designed around 3D-printed cylindrical coupons, then the obtained thermal images are stitched via image processing tool to expose defects from different scans. The analysis employs a Signal-to-Noise Ratio (SNR) metric in an orthogonal tabulation following a Taguchi Design of Experiment. Moreover, test sensitivity and the best combination of factor levels are determined using Analysis of Means (ANOM) and Analysis of Variance (ANOVA). The outcomes show that the heating intensity factor is the most dominant in exposing flaws with close to 40% mean shift and up to 47% variance fluctuation. The paper introduces the tools employed in the study, and then explains the methodology followed to test one sample quadrant. The results for running the testing on all the scenarios are presented, interpreted, and their implications are recommended.

Active thermography has proven successful for non-destructive evaluation of art objects. The aim of this work was to detect the defects of a genuine ancient marquetry sample with both enhanced truncated correlation photothermal coherence tomography (eTC-PCT) and the well-known lock-in thermography (LIT) and compare the two modalities. Quantitative analysis of eTC-PCT images for depth measurements was also performed. Both methods revealed worn-off and glue starved areas, cracks and detachments in the sample, all of which are common defects of these types of art objects. In comparison, the eTC-PCT method was proven to be able to provide 3D images of the interrogated sample and more information about deep sub-surface defects with better contrast than LIT. Moreover, the eTC-PCT results of the marquetry sample containing natural defects were analyzed to determine the location of defects, a task that is not possible with LIT. The experiments showed that both methods are safe for non-destructive evaluation of art samples with temperature rise of less than 0.8 °C using eTC-PCT, and less than 1.5 °C using the LIT method.

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.

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The continuous deterioration of elements, with high patrimonial value over time, can only be mitigated or annulled through the application of techniques that facilitate the preventative detection of the possible agents of deterioration. InfraRed Thermography (IRT) is one of the most used techniques for this task. However, there are few IRT methodologies, which can automatically monitor the cultural heritage field, and are vitally important in eliminating the subjectivity in interpreting and accelerating the analysis process. In this work, a study is performed on a tessellatum layer of a mosaic to automatically: (i) Detect the first appearance of the thermal footprint of internal water, (ii) delimit the contours of the thermal footprint of internal water from its first appearance, and (iii) classify between harmful and non-harmful internal water. The study is based on the analysis of the temperature distribution of each thermal image. Five thermal images sequences are acquired during the simulation of different real situations, obtaining a set of promising results for the optimization of the thermographic inspection process, while discussing the following recommended steps to be taken in the study for future researches. España. Ministerio de Educación I Ref. FPU16/03950 Iberdrola I Ref. Cátedra Iberdrola VIII Centenario - Universidad de Salamanca

In thermographic non-destructive evaluation (TNDE), the optimal thermal stimulus to be provided on precious coatings applied on ancient walls confined in indoor environments is a complex problem to solve particularly when low temperatures characterize the room where they were built. In fact, thermal stresses are always the concern of any restorer who is interested in determining both the positions and sizes of superficial and sub-superficial defects. In scientific literature, the use of lamps appears to be dominant, but when large surfaces must be inspected in situ, many problems could arise, such as environmental reflections, emissivity variations, and non-uniform heating, which cause false alarms in defect detections. In this study, the heating phase was carried out by using a wooden tunnel and an air heater. The main physical and geometrical characteristics of these two objects were optimized in order to stimulate the external coating (called “finishing layer” in this study) as homogeneously as possible. In fact, taking into account the very low temperature of the room which contains the inspected aedicule, the energy deposition (heating phase) is enhanced and the energy dispersion (cooling phase) is reduced (thanks to the tunnel) at the same time. This improves the heat transfer phenomenon along the z axis (i.e., the thickness of the wall). The detection of the defects was performed via higher-order statistics thermography (HOST) technique, which processes the thermal images resulting from the heating and cooling phases. Bearing in mind the low emissivity value of the canary grass (i.e., the finishing layer), the use of heating sources in front of it must be minimized; otherwise, spurious reflections can be recorded and then processed. In particular, a computational fluid dynamics (CFD) approach implemented via Comsol Multiphysics® computer program was used to validate the experimental setup. This was applied to a couple of cases in the Baiocco's room subjected to restrictions of the Superintendence for Historical, Architectural, and Environmental Heritage of the Abruzzo region (Italy) owing to its high artistic importance. The exact position of each defect was modelled after a combined visual-acoustical-thermal inspection. An expert restorer helped us in this task. This study is important because, to the best of our knowledge, the problem discussed above has not been solved yet by the scientific community involved in the field of cultural heritage. The results are first thoroughly discussed and second experimentally validated using a combination between thermocouples and ground penetrating radar (GPR) technique, while advantages and disadvantages of the proposed method are highlighted in view of a future perspective of the present work.

The aim of this research is to investigate the suitability of truncated correlation photothermal coherence tomography (TC-PCT) for the non-destructive imaging of a replica of a real inlay to identify subsurface features that often are invisible areas of vulnerability and damage. Defects of inlays involve glue-rich areas, glue-starved areas, termite attack, insect damage, and laminar splitting. These defects have the potential to result in extensive damage to the art design layers of inlays. Therefore, there is a need for an imaging technique to visualize and determine the location of defects within the sample. The recently introduced TC-PCT modality proved capable of providing 3-D images of specimens with high axial resolution, deep subsurface depth profiling capability, and high signal-to-noise ratio (SNR). Therefore, in this study the authors used TC-PCT to image a fabricated inlay sample with various natural and artificial defects in the middle and top layers. The inlay in question reproduces to scale a piece of art preserved in the “Mirror room” of the Castle Laffitte in France. It was built by a professional restorer following the ancient procedure named element by element. Planar TC-PCT images of the inlay were stacked coherently to provide 3-D visualization of areas with known defects in the sample. The experimental results demonstrated the identification of defects such as empty holes, a hole filled with stucco, subsurface delaminations and natural features such as a wood knot and wood grain in different layers of the sample. For this wooden sample that has a very low thermal diffusivity, a depth range of 2 mm was achieved.

The restoration of Cultural Heritage wouldn't be possible without the financial resources to meet Cultural Heritage needs. A smart procedure to reduce in time and funds spent for restoration is linked to a planning of the diagnostic interventions acting to predict incipient defects undetectable to the naked eye. One of the main methods to fulfil this task is infrared thermography (IRT). The aim of this study is to examine the efficiency of various mathematical techniques in thermographic data processing, with respect to the thermal excitation procedure and the type of artificial defect in a panel painting sample. One of the thermographic analyses performed was based on the pixelwise algorithm for time-derivative of temperature (PATDT). With this algorithm, Newton's cooling law was applied pixel per pixel, resulting in the computation of the cooling rate of each pixel. In addition, the capabilities of the multivariate statistical analysis methods, independent component thermography (ICT) and sparse principal component thermography (SPCT) were also investigated. In the present case study, the authors inspected possible pathologies resembling splitting areas (i.e., detachments) in real panel paintings, with the consequent change in the heat transfer coefficient and the heat capacity. The feasibility of the different analysis methods was illustrated with the application results.

The detection of delaminations in composite laminates using automated thermographic scanning is a quite challenging task. The set-up parameters are not only dependent on the equipment, but on the inspected component as well. In this work, a methodology is discussed to use Finite Element (FE) model updating to automatically establish the most suitable inspection parameters for a given combination of the structure and the investigated delamination depths. The optimised results are compared using binary Probability of Detection analysis and are benchmarked with parameter sets retrieved by an expert using the regular trial & error approach. The results show an improvement of the accuracy and scanning speed which significantly increases as the POD decreases and the complexity of the samples increases.

Non-destructive testing (NDT) techniques play an important role in structural health monitoring (SHM) of composite structures, among which infrared thermography (IRT) is popular because it is easy to operate, enables rapid inspection of large areas, and presents results as easily interpreted thermal images. In order to achieve noise reduction, feature extraction, and data compression, principal component thermography (PCT) was developed for thermographic data processing. However, each principal component in PCT is a linear combination of all the original pixel values, making the results difficult to interpret and hence affecting defect identification. In this work, sparse principal component thermography (SPCT) is proposed as an improved version of PCT, which provides more interpretable analysis results owing to its structure sparsity and leads to a better defect detection. The feasibility of SPCT is illustrated with two case studies.

The use of recycled paper in HDPE (High Density PolyEthylene) matrix composites has recently been introduced as an interesting alternative to traditional recycling process for paper. HDPE is also used as double wall greenhouse glazing because panels are easy to install, UV stabilized, and affordable. These type of products must also be strong enough and durable in order to react under tensile loads provided by wind and harsh weather conditions. An interesting idea may be the insertion via injection moulding of chopped basalt and waste paper – i.e. , two natural products – in pure HDPE samples. It completely follows the environmental sustainability concept centred on the triple “ re ”, i.e. , re cycle, re use and re duce. The research presented herein starts with a Differential Scanning Calorimetry (DSC) inspection of pure HDPE and HDPE mixed with 5% by weight of waste paper plus 5% by weight of chopped basalt as fillers in order to obtain an insight related to the temperature at which possible thermal events (endothermic or exothermic) occur. The dog-bone samples were also inspected under UV conditions (380 nm) before and after tensile tests. The latter approach was simulated firstly by Comsol Multiphysics® computer program, and secondly recorded in real time via thermographic inspections. The temperature variation in a region of interest (ROI) selected at the centre of the samples was mapped in the time during the inspection by infrared thermography (IRT) method using a pseudo-static matrix reconstruction algorithm realized in Matlab® environment. Instead, the combined use of thermocouples aimed at emphasizing the knowledge of the heat transfer in the time due to the mechanical stress applied at the borders of the inspected samples. The aim was to understand whether modified HDPE can (or cannot) be a valid competitor of pure HDPE for the production of semi-transparent and robust panels.

Fibre metal laminates are widely implemented in the aerospace industry owing to the merits of fatigue resistance and plastic properties. An effective defect assessment technique needs to be investigated for this type of composite materials. In order to achieve accurate impact-induced damage evaluation, a multi-excitation infrared fusion method is introduced in this study. Optical excitation thermography with high performance on revealing surface and subsurface defects is combined with vibro-thermography to improve the capability of detection on defects. Quantitative analysis is carried out on the temperature curve to assess the impact-induced deformation. A new image fusion framework including feature extraction, feature selection and fusion steps is proposed to fully utilize the information from two excitation modalities. Six fibre metal laminates which contain aluminium-basalt fibre reinforced plastic and aluminium-glass fibre reinforced plastic are investigated. Features from different perspectives are compared and selected via intensity contrast on deformation area for fusion imaging. Both types of defects (i.e., surface and sub-surface) and the internal deformation situation of these six samples are characterized clearly and intuitively.

Distance estimation and pedestrian detection are critical for safe driving operation decision-making and autonomous vehicle intelligent control strategies. This paper proposes a novel multi-task Faster R-CNN detector which simultaneously realizes distance estimation and pedestrian detection using an improved ResNet-50 architecture. Images were acquired using a near-infrared camera with two near-infrared fill-lights devices during real road nighttime scenarios. Ground truth pedestrian distances used for training were obtained using LIDAR. The data used to optimize the multi-task Faster R-CNN detector were approximately 20 k high-quality near-infrared images with marked pedestrians and tagged distance values. The proposed algorithm including the distance estimation runs at a speed exceeding 7 fps. Pedestrian detection accuracy reached nearly 80% with a total average absolute distance estimation error rate of less than 5%.

Thermal and infrared imagery creates considerable developments in Non-Destructive Testing (NDT) area. Here, a thermography method for NDT specimens inspection is addressed by applying a technique for computation of eigen-decomposition which refers as Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). The proposed approach uses a shorter computational alternative to estimate covariance matrix and Singular Value Decomposition (SVD) to obtain the result of Principal Component Thermography (PCT) and ultimately segments the defects in the specimens applying color based K-medoids clustering approach. The problem of computational expenses for high-dimensional thermal image acquisition is also investigated. Three types of specimens (CFRP, Plexiglas and Aluminium) have been used for comparative benchmarking. The results conclusively indicate the promising performance and demonstrate a confirmation for the outlined properties.

The experimental results from infrared thermography surveys over two buildings externally exposed walls are presented. Data acquisition was performed on a static configuration by recording direct and indirect solar loading during several days and was processed using advanced signal processing techniques in order to increase signal-to-noise ratio and signature contrast of the elements of interest. It is demonstrated that it is possible to detect the thermal signature of large internal structures as well as surface features under such thermographic scenarios. Results from a long-wave microbolometer compared favorably to those from a mid-wave cooled infrared camera for the detection of large subsurface features from unprocessed images. In both cases, however, advanced signal processing greatly improved contrast of the internal features.

Because of the growing need for efficient energy production systems, energy policies promoted in recent years have also involved complex structures, like airports. This paper proposes the implementation of an energy management system for a very energy-consuming structure, composed of different power plants and many energy consumers: the Leonardo da Vinci International Airport of Rome. In this study, the examination of historical data related to airport electric power, thermal energy and fuel consumption is discussed, starting with the analysis of the production energy plants, mainly based on a combined heat and power system. Furthermore, pioneering solutions are proposed, not only to cover airport energy requirements, but also to test the safety and reliability of innovative load management systems. For this reason, the choice of the Leonardo da Vinci management company, oriented to install a smart storage in order to manage the bidirectional energy flows by consumers and producers, is justified. Such innovative energy procurement systems are examined, with the goal of achieving greater penetration of renewable sources: mini and micro wind power plants and high concentrator photovoltaic plants.

Heritage elements, from historic buildings to stone sculptures and panels, stand as key elements in the history of humanity. Unfortunately, the deterioration of both the surface and the interior of these elements is inevitable, endangering the quality and existence of these structures of high historical value in the event of a delay in the implementation of the required maintenance tasks. InfraRed Thermography, IRT, appears as one of the most recent techniques to detect and characterize possible pathologies in structures in their early stages, being very useful for a preventive analysis in heritage elements. This paper presents a methodology for the automatic detection and characterization of one of the most severe and frequent pathologies in heritage structures, moisture, from thermal images. The proposal stands as a demonstration of the potential of the IRT technique for heritage conservation applications, and as a new step towards the automation of the inspection process and optimization of the decision-taking in conservation actions within cultural heritage. For that, two thermal criteria and a semiautomatic image rectification process are implemented as main phases of the methodology, obtaining good results for the detection of moisture zones and accurate area values with regard to the real dimensions of each moisture zone. Specifically, an F-score average of 78%±19% regarding detection performance and a percentage relative error of minimum 4%, and maximum of 12%, referred to the area computation in unit metrics are obtained. Universidad de Salamanca. Cátedra Iberdrola VIII Centenario Ministerio de Educación, Cultura y Deporte | Ref. FPU16/03950

Defects related to degradation were observed in an ancient book paperboard cover through nuclear magnetic resonance relaxometry and infrared thermography. Data collected with this combined method allowed identifying areas with moisture content and thermal diffusivity anomalies within the front board, corresponding to the different conservation status of the cellulose-based material. Non-destructive testing analytical procedures provide comprehensive knowledge for preserving precious library archives.

An Auto-Regressive Exogenous model is proposedfor the feature extraction in Random Photothermal activeThermography. The prosed approach is applied to thenondestructive evaluation of a Carbon Fiber Reinforced Plasticcomposite sample in which delamination was simulated byembedding Teflon tape pieces between the different plies of thestructure. It is shown that the application of the Auto RegressiveExogenous model allows the impulse response of the sample tobe estimated, thus simulating the result of a standard Pulsed Thermography experiment but by using a pseudo-noisemodulated low-power heating source. Such technique opens thepossibility to realize handier setup and to achieve higher SNRvalues than Pulsed-Thermography. For these reasons theproposed approach can find use in the nondestructiveevaluation of aerospace composite components.

This paper presents the contextual use of Pulse-Compression Thermography and Hypercolorimetric Multispectral Imaging for the diagnostic study of historical heritage paintings. The comparison and the integration of images provided by the two techniques allows the conservation state of both the painting layers and wooden support to be investigated. Relevant information on the painting technique and figurative scene can be obtained as well. The proposed approach was applied to two Italian Renaissance panel paintings. The first object tested was a 16th century panel painting representing a Crucifixion, exposed in the Museum of Colle del Duomo in Viterbo, Italy, and attributed to the workshop of the master Michelangelo Buonarroti. The second artwork was a late 15th century panel painting, representing The Resurrection of Christ, currently preserved at Museo Carrara in Bergamo, Italy, and recently re-attributed to Andrea Mantegna; it was identified as being the upper half of a whole composition together with the Descent into Limbo painting. HMI acquisitions and digital image processing tools allowed to investigate the upper painting layer, while PuCT imaging data gave relevant information on the structure of the wooden support proving to be an innovative stratigraphic investigation method. The combination of HMI and PuCT imaging techniques supplied information on the whole structure of the artworks, identifying surface degradation, different layers, wood defects and their position in the inner layers of the object. The integration of the above-mentioned techniques might stand as a new reference diagnostic method to evaluate conservative needs and support decisions for restoration.

A thermographic non-destructive inspection conducted during the winter season on precious walls confined to an indoor environment (Baiocco room, L’Aquila, Italy) is illustrated herein. The use of a heat conveyor ad hoc selected has produced a particular benefit in terms of defect detection after the application of advanced algorithms because the thermal stimulus coming from an air heater hit the finishing layer under a laminar flow regime. Thermocouples registered both the heat-up and cool-down phases to confirm the numerical simulations conducted via Comsol Multiphysics® software. The results obtained by applying the principal component thermography (PCT) technique to raw data registered in the time domain were a priori studied through thermographic simulations. Defects were modelled on the basis of results already shown in a previous work centred on the Baiocco room, too. A comparison between the results obtained from PCT and the innovative sparse principal component thermography (SPCT) technique was also added to the discussion. The SPCT results show their benefits above all when they are supplemented with ground-penetrating radar results. The experimental analyses were conducted on a multi-layer wall called thereafter rectangular portal, in which the instant thermal conductance was experimentally measured. Subsurface cracks were detected using a long heating-up phase. This procedure is preferred by the Monuments and Fine Arts Department with respect to the use of flash lamps. Indeed, short energy pulses should be avoided on finishing layers having low emissivity values because producing spurious reflections affecting the thermographic results. A discussion on this point is also provided. On the basis of the data analysis, it is possible to say that the thermal waves should arrive at the interface between the masonry and its top layer in order to obtain a clear thermal map of the defects present at different depths. As shown, this goal can be realized through a smart solution based on: (a) the inverse knowledge of the aedicula via thermal analyses, (b) the best mesh to be selected for the numerical model, and (c) the optimal type of thermal stress to be delivered to the object under inspection.

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.

Infrared Non-destructive Testing (IRNDT) applications are unequivocally expanded and portend a commodity to improve the quality of defect detection in different fields such as aviation and industrial methods to arts and archaeology. The proposed approach focuses on the application of low-rank sparse principal component thermography (Sparse-PCT or SPCT) to assess the advantages and drawbacks of the method for non-destructive testing. For benchmarking the approach, two types of infrared image sets are tested: the Square Pulse Thermography (SPT) method for two hybrid composites (carbon and flax fiber reinforced epoxy), and passive infrared test of Bell Tower and the University of L’Aquila (AQ) faculty’s wall infrared sets. The quantitative assessment of the approach is also compared for every method and indicate considerable segmentation performance where other similar approaches were not able to detect the defects. SPCT performance was compared to some popular decomposition methods such as principal component thermography (PCT), candid covariance-free incremental principal component thermography (CCIPCT), non-negative matrix factorization (NMF) using gradient descent (GD) or non-negative least square (NNLS). The comparative results demonstrate the considerable performance while the other methods failed.

This study is devoted to the analysis of impact damage in composite samples by applying techniques of IR and Terahertz imaging. The samples made of high-density homogeneous particleboards of sugarcane bagasse and castor oil polyurethane resin were subjected to impacts with the energy from 5 to 30 J. The impact damage defects were evaluated by applying one- and two-sided thermal non-destructive testing. Both the apparent thermal effusivity and diffusivity were determined to analyse their relationship to impact energy. In the above-mentioned range of impact energies, the thermal effusivity varied from 5 to 18%, while the variations in thermal diffusivity were from 4 to 24%. The algorithm of dynamic thermal tomography was used to demonstrate that predominant subsurface cracking occurred at depths up to 1 mm. In addition, the defect areas were tested by applying the Terahertz technique, whose results qualitatively matched the IR thermographic ones to show that the damaged areas were larger than they appeared visually. However, both inspection techniques have revealed no significant dependence of analysed parameters on impact energy. This is believed to prove that non-uniform composite structure is the decisive factor in producing structural defects under impacting.

Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.

In order to study the effects of magnetic memory signals affected by the external magnetic field, the magnetic domain model is used herein to quantify the distribution characteristics of the surface of the magnetic signals linked to the stress concentration areas. By using the method of finite element (FE) analysis, the magneto-mechanical model of X70 pipeline steel was deepened. The change in the rules of magnetic signals in the stress concentration areas subjected to different external magnetic fields was calculated and analyzed, while the systematic research of the experiments was carried out. Research results show that: under the geomagnetic field, there is a one to one linear corresponding relationship between the degree of the stress concentration and the magnetic memory signal. The magnetic memory signal along the different directions changed with the increase of stress, while the trends were different. The ferromagnetic metal components were unsaturated whether the intensity of the external magnetic field was weak, while the intensity of the magnetic field increased with the increase of the magnetic memory signal although the slope of the magneto-mechanics curve decreased. The ferromagnetic metal components were saturated whether the intensity of the external magnetic field was strong, while the magnetic signal on the stress concentration areas was covered and undetectable.

Honeycomb-structured materials are widely used in commercial and military aircraft. Manufacturing defects and damage during operation have become primary safety threats. This has increased the demand for non-destructive testing (NDT) for damage and flaws during aircraft operation and maintenance. Characterizing, or classifying defects, in addition to detecting them, is important. Classifying the liquids trapped in aircraft honeycomb cells is an example. A small amount of ingressed water is often tolerable, whereas a small amount of hydraulic oil may be an early warning of hydraulic system malfunction. This paper proposes an infrared thermography-based NDT technique and a long short term memory recurrent neural network (LSTM-RNN) model which automatically classifies common defects occurring in honeycomb materials. These including debonding, adhesive pooling, and liquid ingress. This LSTM-based algorithm has a greater than 90% sensitivity in classifying water, and hydraulic oil ingress. It has a greater than 70% sensitivity in classifying debonding and adhesive pooling.

The addition of insulating layers on vertical walls of buildings is a common practice for providing a higher thermal insulation of the envelope. Workmanship defects, however, might influence the effectiveness of such insulation strategy. Damaged materials, incorrect installation, use of aged or weathered materials might alter the capability of reducing heat transfer through the envelope, whether vertical or sloped. In this work, drawbacks caused by the wrong installation of insulating material and by damaged material are assessed. A specimen wall was investigated by experimental and numerical approaches, the latter carried out by using COMSOL Multiphysics®. Results are compared and discussed.

Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection. This framework adapts cropping operator and typical component extraction as a preprocessing step to reduce the dimensions of raw data and applies sparse pattern extraction algorithms to enhance the contrast on the defect area. Different cases are studied involving several defects in four basalt-carbon hybrid fiber-reinforced polymer composite laminates. Finally, comparative analysis with intensity distribution is carried out to verify the effectiveness of contrast enhancement using this framework.

The thermal transmittance ( U -value) of the vertical opaque building envelope plays a key role for the evaluation of the thermal performance of a structure. In order to speed up the assessment procedure and to investigate wider portions of buildings directly in situ, new methods based on the use of quantitative infrared thermography have been proposed during the last years. Although the studies agree about the influence of the operative conditions, a detailed report, based on measurements effected on a large wall in a controlled environment and at different boundary conditions, is still missing in literature. The aim of this work is to assess the validity of thermographic methods by using different operative conditions in a controlled environment. The results obtained by different IR methods in a Guarded Hot Box have been compared with heat flow meter measurements and theoretical values.

The object of the work is a character of the Madonna con Bambino (XIII–XV century) mural painting (Fontecchio – L’Aquila, Italy). It was analyzed by different nondestructive testing (NDT) techniques: electronic speckle pattern interferometry (ESPI), ultraviolet (UV) imaging and infrared vision. In addition, three micro-samplings were collected on suspected areas after examination of the signal strength variations over the raw thermograms. On the latter, the images’ quality was enhanced by applying advanced processing techniques. Micro-samplings were also analyzed by scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared (FTIR) and μ-Raman spectroscopy. Splitting, subsurface cracks and under-/over-paintings were detected by this integrated method.

This work presents a non-destructive analysis based on infrared thermography (IRT) and speckle inspection methods performed on a helicopter blade. In both cases, thermal stresses are needed in order to provoke the visualization of defects. The sample possess fabricated defects, and three techniques were selected, namely, long-pulse thermography, flash thermography, and digital speckle photography (DSP) to retrieve their positions. The first two techniques belong to the group of infrared imaging, that are inherent to the analysis of the infrared thermal patterns to detect internal anomalies in the material, whilst the last one corresponds to the optical imaging group that requires visible light to measure the material response under a thermal stimulus. The active approach, useful to produce a gradient in either, thermal and/or displacement field of the material, was used. In all cases, heat lamps were used to generate the required thermal stresses. Post-processing algorithms were applied to raw data in order to improve the defect detection. Results are finally compared to evaluate pros and cons of each method.

The recent applications in the field of thermography and Infrared Non-Destructive Testing (IRNDT) involved many different research fields, and in most of these applications well-known infrared approaches have been utilized for thermal image enhancement, thermal image segmentation, and particularly defect segmentation in IRNDT. Principal Component Analysis (PCA) or Principal Component Thermography (PCT) is one of these methods that has been countlessly used and it is unequivocally one of the constantly referred approaches in this field. Unfortunately, it suffers from being a linear transformation and besides that, finding appropriate basis through its eigenimage decomposition is the shortcoming. Here, an application of non-linear eigen decomposition using Sparse Principal Component Analysis/Thermography (Sparse-PCA or Sparse-PCT) is addressed for segmentation of defects inherent to two hybrid composites (carbon and flax fiber epoxy prepregs). The results indicate considerable segmentation performance when it is compared to similar approaches.

In this paper, eddy current pulsed thermography in transmission mode was used to detect the damages caused by low-velocity impacts in carbon fiber-reinforced polymer and basalt-carbon hybrid fiber-reinforced polymer laminates. In particular, different hybrid structures including intercalated stacking and sandwich-like structures were used. The impact energy of 12.5 was used for the evaluation of the impact damage level. Ultrasonic phased-array C-scan was performed for comparative purposes. In addition, the advantages and disadvantages of the two structures were identified and discussed.

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

As an emerging nondestructive diagnostic and monitoring technique, terahertz time-domain spectroscopy (THz-TDS) imagery is attracting more attention. In this regard, new THz image processing algorithms based on infrared thermography (IRT) concepts are greatly needed, since most IRT imagery modalities are fast for in-line industrial inspection. However, this scenario is difficult due to some physical constraints to be reached, although this idea should be followed to avoid the loss of useful information during image processing. In this paper, a novel THz amplitude polynomial principle component regression (APPCR) algorithm is proposed for the inspection of aramid–basalt hybrid composite laminates. This algorithm segments THz amplitude–frequency curves to simulate heating-up and cooling-down behaviors as in IRT; in addition, it uses an empirical orthogonal functions-based principle component regression modality to simplify the THz image analysis procedure. This experimental and analytical study shows that APPCR can, first, simplify the THz image analysis procedure, and second, enhance image contrast and spatial resolution. A theoretical analysis was conducted as experimental explanation, while the IRT imagery results were used for comparative purposes. In addition, signal-to-noise ratio analysis was used to evaluate quantitatively the image enhancement. Finally, it is possible to conclude that THz is more suitable to inspect transparent or semitransparent materials. Advantages and disadvantages of THz-TDS and IRT are summarized in the text.

The objective of this investigation is to assess the IR thermography as a monitoring system able to detect the grinding conditions in order to test its use as an industrial tool for optimizing and control the process. To this aim an experimental investigation has been carried out in the grinding of Metal Matrix Composites (MMCs). These materials exhibit additional drawbacks with respect to conventional materials due to the abrasive nature of the reinforcement together with the softness of the matrix. The results show how the IR thermography can give a significant contribution in the definition of a strategy to control the grinding process as well as for the maintenance of the grinding machine.

Contemporary thermoset composites using natural fibres offer a wide range of strength performance. Recently, the combination of flax and carbon fibres has received an increasing attention, mainly dictated by the possibility of merging in a single material high damping properties of flax fibres and the well-known high mechanical properties of carbon fibres. Evaluation of low energy impact damage defects has received a little coverage even if these composites are well known to be susceptible to impact damage. In this study, the use of ultrasonic resonance vibrometry is proposed as an effective nondestructive tool to detect the extent of impact damage in natural/synthetic fibre reinforced composites with different stacking sequences. The results for impacts at 10 and 40 J highlighted the role played by the different stacking sequences with damaged areas being twice smaller in composites with flax skins and carbon core compared to carbon-flax-carbon sandwiches.

In this paper, eddy current pulsed thermography was used to evaluate ballistic impact damages in basalt-carbon hybrid fiber-reinforced polymer composite laminates for the first time, to our knowledge. In particular, different hybrid structures including intercalated stacking and sandwich-like sequences were used. Pulsed phase thermography, wavelet transform, principle component thermography, and partial least-squares thermography were used to process the thermographic data. Ultrasound C-scan testing and X-ray computed tomography were also performed for comparative purposes. Finite element analysis was used for validation. Finally, an analytical and comparative study was conducted based on signal-to-noise ratio analysis.

In this work, the thermal potentialities of insulation panels made of cork have been explored by means of a numerical approach based on experimental data. A comparative analysis between the panel in unaltered state, and then covered with an innovative shield coating (COIB 250®) was carried out. In addition, a defect simulating an inner detachment of the panel was fabricated to understand its behaviour during a daily solar thermal load. The site was selected ad hoc to avoid any shadow cast effect on the panel itself and any conduction phenomenon from the surrounding area. The external floor on which the panel was mounted was completely isolated from the soil. Two similar days with clear sky conditions were selected, real meteorological data recorded by a weather station installed near the inspected site and data deriving from a NASA software were used respectively for the ambient temperature and for the solar radiation, in order to provide a solid discussion of the findings. Results show how a cork panel, usually employed in civil engineering as an insulation system, may benefit a lot of a shield coating. The latter product tends also to minimize the impact of a subsurface detachment during the thermal conduction via heat transfer; this behaviour will be in-depth clarified in this work.

Recently, basalt-carbon hybrid composite structures have attracted increasing attention due to their comprehensive mechanical performance, if compared to carbon fiber reinforced polymer composites (CFRP). Low-velocity is considered as one of the most severe threats to composite products, since it is usually invisible and it occurs frequently in service. In this regard, non-destructive testing (NDT) techniques, especially emerging modalities, are expected to be an effective damage detection method. In this paper, eddy current pulsed thermography (ECPT), as an emerging NDT technique, was used to evaluate the damage induced by low-velocity impact loading in a CFRP laminate, as well as in two different-structured basalt-carbon hybrid composite laminates. In addition, ultrasonic C-scan and X-ray computed tomography were performed to validate the thermographic results. Pulsed phase thermography, principal component thermography and partial least squares thermography were used to process the thermal data and to retrieve the damage imagery. Then, a further analysis was performed on the imagery and temperature profile. As a result, it is concluded that ECPT is an effective technique for hybrid composite evaluation. The impact energy tends to create an interlaminar damage in a sandwich-like structure, while it tends to create an intralaminar damage in an intercalated stacking structure.