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5 pages, 168 KiB

Open AccessEditorial

Infrared Imaging and NDT

by Rubén Usamentiaga and Pablo Venegas

Appl. Sci. 2021, 11(7), 3024; https://doi.org/10.3390/app11073024 - 29 Mar 2021

Viewed by 1448

Abstract

Infrared thermography has become a mature and widely accepted technology with applications in many different fields, from medical to industrial [...] Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

Research

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15 pages, 3582 KiB

Open AccessArticle

Dynamic Line Scan Thermography Optimisation Using Response Surfaces Implemented on PVC Flat Bottom Hole Plates

by Simon Verspeek, Jona Gladines, Bart Ribbens, Xavier Maldague and Gunther Steenackers

Appl. Sci. 2021, 11(4), 1538; https://doi.org/10.3390/app11041538 - 8 Feb 2021

Cited by 6 | Viewed by 1708

Abstract

Nowadays, performing dynamic line scan thermography (DLST) is very challenging, and therefore an expert is needed in order to predict the optimal set-up parameters. The parameters are mostly dependent on the material properties of the object to be inspected, but there are also [...] Read more.

Nowadays, performing dynamic line scan thermography (DLST) is very challenging, and therefore an expert is needed in order to predict the optimal set-up parameters. The parameters are mostly dependent on the material properties of the object to be inspected, but there are also correlations between the parameters themselves. The interrelationship is not always evident even for someone skilled in the art. Therefore, optimisation using response surface can give more insights in the interconnections between parameters, but also between the material properties and the variables. Performing inspections using an optimised parameter set will result in high contrast thermograms showing the size and shape of the defect accurately. Using response surfaces to predict the optimal parameter set enables to perform fast measurements without the need of extensive testing to find adequate measurement parameters. Differing from the optimal parameters will result in contrast loss or detail loss of the size and shape of the detected defect. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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17 pages, 6496 KiB

Open AccessArticle

Inductive Thermography as Non-Destructive Testing for Railway Rails

by Christoph Tuschl, Beate Oswald-Tranta and Sven Eck

Appl. Sci. 2021, 11(3), 1003; https://doi.org/10.3390/app11031003 - 22 Jan 2021

Cited by 19 | Viewed by 3790

Abstract

Inductive thermography is a non-destructive testing method, whereby the specimen is slightly heated with a short heating pulse (0.1–1 s) and the temperature change on the surface is recorded with an infrared (IR) camera. Eddy current is induced by means of high frequency [...] Read more.

Inductive thermography is a non-destructive testing method, whereby the specimen is slightly heated with a short heating pulse (0.1–1 s) and the temperature change on the surface is recorded with an infrared (IR) camera. Eddy current is induced by means of high frequency (HF) magnetic field in the surface ‘skin’ of the specimen. Since surface cracks disturb the eddy current distribution and the heat diffusion, they become visible in the IR images. Head checks and squats are specific types of damage in railway rails related to rolling contact fatigue (RCF). Inductive thermography can be excellently used to detect head checks and squats on rails, and the method is also applicable for characterizing individual cracks as well as crack networks. Several rail pieces with head checks, with artificial electrical discharge-machining (EDM)-cuts and with a squat defect were inspected using inductive thermography. Aiming towards rail inspection of the track, 1 m long rail pieces were inspected in two different ways: first via a ‘stop-and-go’ technique, through which their subsequent images are merged together into a panorama image, and secondly via scanning during a continuous movement of the rail. The advantages and disadvantages of both methods are compared and analyzed. Special image processing tools were developed to automatically fully characterize the rail defects (average crack angle, distance between cracks and average crack length) in the recorded IR images. Additionally, finite element simulations were used to investigate the effect of the measurement setup and of the crack parameters, in order to optimize the experiments. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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24 pages, 2540 KiB

Open AccessArticle

Quaternion Processing Techniques for Color Synthesized NDT Thermography

by Pablo Venegas, Rubén Usamentiaga, Juan Perán and Idurre Sáez de Ocáriz

Appl. Sci. 2021, 11(2), 790; https://doi.org/10.3390/app11020790 - 15 Jan 2021

Cited by 3 | Viewed by 1822

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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29 pages, 8049 KiB

Open AccessArticle

Automatic Extraction of Material Defect Size by Infrared Image Sequence

by Lihua Yuan, Xiao Zhu, Quanbin Sun, Haibo Liu, Peter Yuen and Yonghuai Liu

Appl. Sci. 2020, 10(22), 8248; https://doi.org/10.3390/app10228248 - 20 Nov 2020

Cited by 8 | Viewed by 2539

Abstract

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame [...] Read more.

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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17 pages, 9096 KiB

Open AccessArticle

An Experimental Study on the Defect Detectability of Time- and Frequency-Domain Analyses for Flash Thermography

by Gaétan Poelman, Saeid Hedayatrasa, Joost Segers, Wim Van Paepegem and Mathias Kersemans

Appl. Sci. 2020, 10(22), 8051; https://doi.org/10.3390/app10228051 - 13 Nov 2020

Cited by 13 | Viewed by 2256

Abstract

A defect’s detectability in flash thermography is highly dependent on the applied post-processing methodology. The majority of the existing analysis techniques operate either on the time-temperature data or on the frequency-phase data. In this paper, we compare the efficiency of time- and frequency-domain [...] Read more.

A defect’s detectability in flash thermography is highly dependent on the applied post-processing methodology. The majority of the existing analysis techniques operate either on the time-temperature data or on the frequency-phase data. In this paper, we compare the efficiency of time- and frequency-domain analysis techniques in flash thermography for obtaining good defect detectability. Both single-bin and integrated-bin evaluation procedures are considered: dynamic thermal tomography and thermal signal area for the time-domain approach, and frequency domain tomography and adaptive spectral band integration for the frequency-domain approach. The techniques are applied on various carbon fiber reinforced polymer samples having a range of defect sizes and defect types. The advantages and drawbacks of the different post-processing techniques are evaluated and discussed. The best defect detectability is achieved using the integrated procedure in frequency domain. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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21 pages, 4022 KiB

Open AccessArticle

Infrared Image Adaptive Enhancement Guided by Energy of Gradient Transformation and Multiscale Image Fusion

by Feiran Chen, Jianlin Zhang, Jingju Cai, Tao Xu, Gang Lu and Xianrong Peng

Appl. Sci. 2020, 10(18), 6262; https://doi.org/10.3390/app10186262 - 9 Sep 2020

Cited by 8 | Viewed by 2493

Abstract

The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display [...] Read more.

The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display equipment with appropriate contrast and clear detail information. The steps are chiefly as follows. First, in order to protect the weak global details in different regions of the image, we adjust the original normalized image into multiple brightness levels by adaptive Gamma transformation. Second, each brightness image is decomposed into a base layer and several detail layers by the multiscale guided filter. Details in each image are enhanced separately. Third, to obtain the image with global details of the input image, enhanced images in each brightness are fused together. Last, we filter out the outliers and adjust the dynamic range before outputting the image. Compared with other conventional or cutting-edge methods, the experimental results demonstrate that the proposed approach is effective and robust in dynamic range compression and detail information enhancement of IR image. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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18 pages, 34531 KiB

Open AccessArticle

Robust Detection, Classification and Localization of Defects in Large Photovoltaic Plants Based on Unmanned Aerial Vehicles and Infrared Thermography

by Alberto Fernández, Rubén Usamentiaga, Pedro de Arquer, Miguel Ángel Fernández, D. Fernández, Juan Luis Carús and Manés Fernández

Appl. Sci. 2020, 10(17), 5948; https://doi.org/10.3390/app10175948 - 27 Aug 2020

Cited by 27 | Viewed by 3682

Abstract

The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, [...] Read more.

The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, PV plant monitoring is carried out by either electrical performance measurements or image processing. The first approach presents limited fault detection ability, it is costly and time-consuming, and it is incapable of fast identification of the physical location of the fault. In the second approach, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures, but their setup and processing are rather complex and an experienced technician is required. The use of Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules has been identified as a cost-effective approach that offers 10–-15 fold lower inspection times than conventional techniques. However, previous works have not performed a comprehensive approach in the context of automated UAV inspection using IRT. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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16 pages, 4977 KiB

Open AccessFeature PaperArticle

Characterizing Subsurface Rectangular Tilted Heat Sources Using Inductive Thermography

by Arantza Mendioroz, Lorenzo Fuggiano, Pablo Venegas, Idurre Sáez de Ocáriz, Umberto Galietti and Agustín Salazar

Appl. Sci. 2020, 10(16), 5444; https://doi.org/10.3390/app10165444 - 6 Aug 2020

Cited by 4 | Viewed by 1788

Abstract

In this study, we characterize the lateral dimension, depth, and inclination of buried tilted rectangular heat sources from time domain temperature data measured at the surface. The heat sources are representative for planar defects that emit heat in thermographic tests with internal burst [...] Read more.

In this study, we characterize the lateral dimension, depth, and inclination of buried tilted rectangular heat sources from time domain temperature data measured at the surface. The heat sources are representative for planar defects that emit heat in thermographic tests with internal burst excitation. We present a semi-analytical expression for the evolution of the surface temperature distribution. The emitted flux, dimensions and inclination of the heat source are determined by fitting the model to two perpendicular surface temperature profiles and the temperature history at one point of the surface. We show that the sensitivity of the data to the geometrical parameters of the heat source decreases as the angle it makes with the surface increases. The study also shows that the optimum duration of the excitation corresponds to a thermal diffusion length covering the distance from the surface to the deepest end of the heat source. The accuracy and precision of the results for different noise levels and inclinations have been tested by fitting the model to synthetic data with added noise. Fittings of experimental induction thermography data on 3D printed photo-polymeric resin samples containing calibrated Cu slabs confirm that it is possible to characterize tilted rectangular heat sources from surface temperature data. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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13 pages, 1904 KiB

Open AccessArticle

Cluster Analysis of IR Thermography Data for Differentiating Glass Types in Historical Leaded-Glass Windows

by Michaël Hillen, Stijn Legrand, Yarince Dirkx, Koen Janssens, Geert Van der Snickt, Joost Caen and Gunther Steenackers

Appl. Sci. 2020, 10(12), 4255; https://doi.org/10.3390/app10124255 - 21 Jun 2020

Cited by 5 | Viewed by 2441

Abstract

Infrared thermography is a fast, non-destructive and contactless testing technique which is increasingly used in heritage science. The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types [...] Read more.

Infrared thermography is a fast, non-destructive and contactless testing technique which is increasingly used in heritage science. The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of historical glass that were included in a colorless leaded-glass windows during previous restoration interventions. Inspection of the thermograms and the application of two data mining techniques on the thermal data, i.e., k-means clustering and hierarchical clustering, allowed identifying different groups of window panes that show a different thermal behavior. Both clustering approaches arrive at similar groupings of the glass with a clear separation of three types. However, the lead cames that hold the glass panes appear to have a substantial impact on the thermal behavior of the surrounding glass, thus preventing classification of the smallest glass panes. For the larger panes, this was not a critical issue as the center of the glass remained unaffected. Subtle visual color differences between panes, implying a variation in coloring metal ions, was not always distinguished by IRT. Nevertheless, data clustering assisted infrared thermography shows potential as an efficient and swift method for documenting the material intervention history of leaded-glass windows during or in preparation of conservation treatments. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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15 pages, 16195 KiB

Open AccessFeature PaperArticle

Low Thermal Conductivity Materials and Very Low Heat Power: A Demanding Challenge in the Detection of Flaws in Multi-Layer Wooden Cultural Heritage Objects Solved by Pulse-Compression Thermography Technique

by Stefano Sfarra, Stefano Laureti, Gianfranco Gargiulo, Hamed Malekmohammadi, Mario Andrea Sangiovanni, Mauro La Russa, Pietro Burrascano and Marco Ricci

Appl. Sci. 2020, 10(12), 4233; https://doi.org/10.3390/app10124233 - 20 Jun 2020

Cited by 15 | Viewed by 2930

Abstract

An inlay sample with artificial defects was inspected via the pulse-compression thermography (PuCT) technique. The sample belongs to the cultural heritage field, and it was realized by a professional restorer based on his long-time experience, imitating historical art crafting styles. The tesserae composing [...] Read more.

An inlay sample with artificial defects was inspected via the pulse-compression thermography (PuCT) technique. The sample belongs to the cultural heritage field, and it was realized by a professional restorer based on his long-time experience, imitating historical art crafting styles. The tesserae composing the inlay were not treated by any protective paints, so that external thermal stimuli may cause physical/mechanical alterations of the cell walls, with consequent colour changes, cracks, and eventually damage to its surface. To avoid any alteration of the sample, the PuCT technique was used for inspecting the inlay sample as it allows the heating power to be very low, while assuring enough thermal contrast for the defects to be detected after the exploitation of the pulse-compression algorithm. Even if a maximum ΔT slightly exceeding 1 °C was detected during the PuCT test of the inlay sample, it is shown that this is enough for detecting several defects. Further, image processing based on the Hilbert transform increases defect detection and characterization. In addition, a novel normalization technique, i.e., a pixel-by-pixel data normalization with respect to the absorbance estimated by considering a characteristic value of the compression peak, is introduced here for the first time. The proposed normalization enhances the defect detection capability with respect to the standard pixel-by-pixel amplitude visualization. This has been demonstrated for two experimental setups, both exploiting the same LED chips system as heating source but different thermal camera sensors, i.e., one in the mid-infrared spectrum, the other in the far infrared one. Thus, the present work is also the first small-scale test of a future portable system that will include low-power LED chip feed in DC by metal-oxide-semiconductor field-effect transistor (MOSFET) devices, and a handy far-infrared camera. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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11 pages, 18142 KiB

Open AccessArticle

Estimation of Thermal Resistance Field in Layered Materials by Analytical Asymptotic Method

by Marie-Marthe Groz, Mohamed Bensalem, Alain Sommier, Emmanuelle Abisset-Chavanne, Stéphane Chevalier, Arsenii Chulkov, Jean-Luc Battaglia, Jean-Christophe Batsale and Christophe Pradere

Appl. Sci. 2020, 10(7), 2351; https://doi.org/10.3390/app10072351 - 30 Mar 2020

Cited by 6 | Viewed by 2225

Abstract

In this paper, the problem of the quantitative characterization of thermal resistance fields in a multilayer sample is addressed by using the classical front face flash method as the thermal excitation and infrared thermography (IRT) as the monitoring sensor. In this challenging problem, [...] Read more.

In this paper, the problem of the quantitative characterization of thermal resistance fields in a multilayer sample is addressed by using the classical front face flash method as the thermal excitation and infrared thermography (IRT) as the monitoring sensor. In this challenging problem, the complete inverse processing of a multilayer analytical model is difficult due to the lack of sensitivity of some parameters (layer thickness, depth of thermal resistance, etc.) and the expansive computational iterative processing. For these reasons, the proposed strategy is to use a simple multilayer problem where only one resistive layer is estimated. Moreover, to simplify the inverse processing often based on iterative methods, an asymptotic development method is proposed here. Regarding the thermal signal reconstruction (TSR) methods, the drawback of these methods is the inability to be quantitative. To overcome this problem, the method incorporates a calibration process originating from the complete analytical quadrupole solution to the thermal problem. This analytical knowledge allows self-calibration of the asymptotic method. From this calibration, the quantitative thermal resistance field of a sample can be retrieved with a reasonable accuracy lower than 5%. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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19 pages, 4541 KiB

Open AccessArticle

Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

by Qi Wang, Xiang Gao, Fan Wang, Zhihang Ji and Xiaopeng Hu

Appl. Sci. 2020, 10(7), 2302; https://doi.org/10.3390/app10072302 - 27 Mar 2020

Cited by 10 | Viewed by 3427

Abstract

Infrared and visible image match is an important research topic in the field of multi-modality image processing. Due to the difference of image contents like pixel intensities and gradients caused by disparate spectrums, it is a great challenge for infrared and visible image [...] Read more.

Infrared and visible image match is an important research topic in the field of multi-modality image processing. Due to the difference of image contents like pixel intensities and gradients caused by disparate spectrums, it is a great challenge for infrared and visible image match in terms of the detection repeatability and the matching accuracy. To improve the matching performance, a feature detection and description method based on consistent edge structures of images (DDCE) is proposed in this paper. First, consistent edge structures are detected to obtain similar contents of infrared and visible images. Second, common feature points of infrared and visible images are extracted based on the consistent edge structures. Third, feature descriptions are established according to the edge structure attributes including edge length and edge orientation. Lastly, feature correspondences are calculated according to the distance of feature descriptions. Due to the utilization of consistent edge structures of infrared and visible images, the proposed DDCE method can improve the detection repeatability and the matching accuracy. DDCE is evaluated on two public datasets and are compared with several state-of-the-art methods. Experimental results demonstrate that DDCE can achieve superior performance against other methods for infrared and visible image match. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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15 pages, 4352 KiB

Open AccessArticle

Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution

by Lei Yu, Xuewei Zhang and Yan Chu

Appl. Sci. 2020, 10(3), 1109; https://doi.org/10.3390/app10031109 - 7 Feb 2020

Cited by 9 | Viewed by 2689

Abstract

In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convolution algorithm are combined to enrich the details; then, a regularization function [...] Read more.

In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convolution algorithm are combined to enrich the details; then, a regularization function with two adaptive parameters and two regularization terms is proposed to enhance the edge. MPSR firstly enhances the multi-scale detail of low-resolution images; then, regular processing and feature extraction are carried out; finally, sub-pixel convolution is used to fuse the extracted features to generate high-resolution images. The experimental results show that the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results. Full article

(This article belongs to the Special Issue Infrared Imaging and NDT)

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