Showing papers on "Thermography published in 2021"

Hybrid 0D Antimony Halides as Air-Stable Luminophores for High-Spatial-Resolution Remote Thermography.

Abstract: Luminescent organic-inorganic low-dimensional ns2 metal halides are of rising interest as thermographic phosphors. The intrinsic nature of the excitonic self-trapping provides for reliable temperature sensing due to the existence of a temperature range, typically 50-100 K wide, in which the luminescence lifetimes (and quantum yields) are steeply temperature-dependent. This sensitivity range can be adjusted from cryogenic temperatures to above room temperature by structural engineering, thus enabling diverse thermometric and thermographic applications ranging from protein crystallography to diagnostics in microelectronics. Owing to the stable oxidation state of Sb3+ , Sb(III)-based halides are far more attractive than all major non-heavy-metal alternatives (Sn-, Ge-, Bi-based halides). In this work, the relationship between the luminescence characteristics and crystal structure and microstructure of TPP2 SbBr5 (TPP = tetraphenylphosphonium) is established, and then its potential is showcased as environmentally stable and robust phosphor for remote thermography. The material is easily processable into thin films, which is highly beneficial for high-spatial-resolution remote thermography. In particular, a compelling combination of high spatial resolution (1 µm) and high thermometric precision (high specific sensitivities of 0.03-0.04 K-1 ) is demonstrated by fluorescence-lifetime imaging of a heated resistive pattern on a flat substrate, covered with a solution-spun film of TPP2 SbBr5 .

A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.

Abstract: Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.

Thermal biology in river buffalo in the humid tropics: neurophysiological and behavioral responses assessed by infrared thermography

Abstract: aNeurophysiology, behaviour and animal welfare assessment, DPAA, Universidad Autónoma Metropolitana (UAM), Xochimilco campus, 04960, Mexico City, Mexico. bScuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università degli Studi della Basilicata, 85100, Potenza, Italy. cEmeritus Professor Universidad Autónoma Metropolitana-Iztapalapa, (UAM-I), Deparment of Biotechnology: Food Science, 09340, Mexico City, Mexico. dGraduate and Research Department, Faculty of Veterinary Medicine, Universidad Autónoma de Tamaulipas, Ciudad Victoria, Tamaulipas, Mexico. eDepartment of Food Science. Universidad Autónoma Metropolitana (UAM-L), Campus Lerma, 52005, Lerma City, Mexico. fLivestock Production, Colegio de Postgraduados, Montecillo, 56230, Texcoco, México. gLivestock Science Department, Universidad Nacional Autónoma de México (UNAM), FESC, 54714, State of Mexico, Mexico. hFacultad de Ciencias Agropecuarias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México.

A Smart Thermography Camera and Application in the Diagnosis of Electrical Equipment

Abstract: The thermography camera is widely used to inspect electrical equipment. The existing defect assessment indicators are mainly the hot spot temperature and the relative temperature difference (RTD), and the analyzing process used to calculate the indicators is usually off-site. A smart thermography camera is designed and its application in the diagnosis of electrical equipment is investigated in this article. For the camera, the regional RTD is calculated automatically based on equipment detection and image registration algorithms, and the defects can be judged according to the existing criteria. Firstly, the regional RTD and its implementation method are proposed to reduce the error when the hot spot is unmeasurable. Then the object detection method based on the convolutional neural network (CNN) is modified dedicated to infrared (IR) images. Over 12 000 historical images and 18 000 labels are used for training and tests. The modified model can identify 16 classes of substation equipment with 86.4% mAP. With low latency, the inference speed of this on-site smart camera reaches 30 frames/s. The test results show that the similarity of diagnosis result between our method and the criteria is 92.2%.

Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications

Abstract: Infrared thermography is a growing technology in the engineering geological field both for the remote survey of rock masses and as a laboratory tool for the non-destructive characterization of intact rock. In this latter case, its utility can be found either from a qualitative point of view, highlighting thermal contrasts on the rock surface, or from a quantitative point of view, involving the study of the surface temperature variations. Since the surface temperature of an object is proportional to its emissivity, the knowledge of this last value is crucial for the correct calibration of the instrument and for the achievement of reliable thermal outcomes. Although rock emissivity can be measured according to specific procedures, there is not always the time or possibility to carry out such measurements. Therefore, referring to reliable literature values is useful. In this frame, this paper aims at providing reference emissivity values belonging to 15 rock types among sedimentary, igneous and metamorphic categories, which underwent laboratory emissivity estimation by employing a high-sensitivity thermal camera. The results show that rocks can be defined as “emitters”, with emissivity generally ranging from 0.89 to 0.99. Such variability arises from both their intrinsic properties, such as the presence of pores and the different thermal behavior of minerals, and the surface conditions, such as polishing treatments for ornamental stones. The resulting emissivity values are reported and commented on herein for each different studied lithology, thus providing not only a reference dataset for practical use, but also laying the foundation for further scientific studies, also aimed at widening the rock aspects to investigate through IRT.

Thermographic inspection of water ingress in composite honeycomb sandwich structure: a quantitative comparison among Lock-in thermography algorithms

Abstract: The increasing use of honeycomb structures in the aircraft industry has demonstrated the need for the development of more effective evaluation methodologies. In recent years, thermography has recei...

Theoretical basics of radiant heat transfer – practical examples of calculation for the infrared (IR) used in infrared thermography measurements

Abstract: The article presents the theoretical basics and definitions of radiant heat transfer with practical examples of calculation for the infrared (IR) used in infrared thermography measurements. In orde...

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

Abstract: 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.

Inductive Thermography as Non-Destructive Testing for Railway Rails

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 (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.

A Deep Learning Method for the Impact Damage Segmentation of Curve-Shaped CFRP Specimens Inspected by Infrared Thermography.

Abstract: Advanced materials such as continuous carbon fiber-reinforced thermoplastic (CFRP) laminates are commonly used in many industries, mainly because of their strength, stiffness to weight ratio, toughness, weldability, and repairability. Structural components working in harsh environments such as satellites are permanently exposed to some sort of damage during their lifetimes. To detect and characterize these damages, non-destructive testing and evaluation techniques are essential tools, especially for composite materials. In this study, artificial intelligence was applied in combination with infrared thermography to detected and segment impact damage on curved laminates that were previously submitted to a severe thermal stress cycles and subsequent ballistic impacts. Segmentation was performed on both mid-wave and long-wave infrared sequences obtained simultaneously during pulsed thermography experiments by means of a deep neural network. A deep neural network was trained for each wavelength. Both networks generated satisfactory results. The model trained with mid-wave images achieved an F1-score of 92.74% and the model trained with long-wave images achieved an F1-score of 87.39%.

3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

Abstract: The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

Deep convolutional neural networks for classifying breast cancer using infrared thermography

Abstract: Infrared thermography is a technique that can detect anomalies in temperature patterns which can indicate some breast pathologies including breast cancer. One limitation of the method is the absenc...

Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

Abstract: In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

Infrared Thermography in Metallic Environments of WEST and ASDEX Upgrade

Abstract: Infra-red (IR) thermography is a widely used tool in fusion devices to monitor and to protect the plasma-facing component (PFC) from excessive heat loads. However, with the use of all-metal walls in fusion devices, deriving surface temperature from IR measurements has become more challenging. In this paper, an overview of infra-red measurements in the metallic tokamaks WEST and ASDEX Upgrade (AUG) is reported and the techniques carried out in the modeling and experimental fields to deal with this radiative and fully reflective environment are presented. Experimental characterizations of metallic samples in laboratory and experiments in WEST and AUG reveal that the behavior of both the emission and the reflectance can vary significantly with surface roughness, machining process and as the plasma operation progress. In parallel, the development of a synthetic IR diagnostic has allowed for a better interpretation of the IR images by assessing the reflection patterns and their origin. This has also proven that small-scale change in the emission pattern of beveled PFC can be confused with abnormal thermal events. Numerical solutions to evaluate the contribution of the reflections associated with a variable emissivity in a fully reflective and radiative environment are finally presented.

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

Abstract: 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.