Showing papers on "Thermography published in 2022"

A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings

Abstract: This paper provides a comprehensive review on the use of infrared thermography to detect delamination on infrastructures and buildings. Approximately 200 pieces of relevant literature were evaluated, and their findings were summarized. The factors affecting the accuracy and detectability of infrared thermography were consolidated and discussed. Necessary measures to effectively capture latent defects at the early stage of delamination before crack formation were investigated. The results of this study could be used as the benchmarks for setting standardized testing criteria as well as for comparison of results for future works on the use of infrared thermography for detection of delamination on infrastructures and buildings.

Inspection and condition monitoring of large-scale photovoltaic power plants: A review of imaging technologies

Abstract: The massive growth of PV farms, both in number and size, has motivated new approaches in inspection system design and monitoring. This paper presents a review of imaging technologies and methods for analysis and characterization of faults in photovoltaic (PV) modules. The paper provides a brief overview of PV system (PVS) reliability studies and monitoring approaches where fault related PVS power loss is evaluated. Research on infrared thermography (IRT) and luminescence imaging technologies is thoroughly reviewed, with focus on ease of implementation, efficiency and unmanned aerial system (UAS) compatibility. Furthermore, the review will provide novel insight into state-of-the-art electroluminescence (EL), photoluminescence (PL) and ultraviolet fluorescence (UVF) imaging, and how to interpret these images. The development of imaging techniques will continue to be an attractive domain of research that can be combined with aerial scanning for a cost-effective remote inspection that enable reliable power production in large-scale PV plants.

Depth quantification of rolling contact fatigue crack using skewness of eddy current pulsed thermography in stationary and scanning modes

Abstract: Eddy current pulsed thermography (ECPT) has been used in the characterization of Rolling Contact Fatigue (RCF) cracks in rail by taking advantage of electromagnetic thermal execution. However, quantitative estimation of depths of RCF defects remains challenging due to volume eddy current heating and lateral thermal diffusion under the stationary and scanning inspections. This work proposes math-physic modelling of skewness to quantitatively characterize crack depth in a rail sample. In particular, a physical model linked instrument describing the accumulation of Joule heating via eddy current accompanied by heat diffusion and mathematical skewness has been established. A comparison between the accuracy of crack depth determination in stationary and scanning modes was carried out in terms of the thermal response and skewness. Moreover, the effects of crack types on depth quantification have been analyzed. The comparative experimental results indicated that the skewness under stationary conditions is more robust against the noise, and this has verified the efficiency for quantifying RCF cracks in stationary and scanning modes.

Infrared thermography in the built environment: A multi-scale review

Abstract: The paper presents a review on major contributions in infrared thermography to study the built environment at multiple scales. To elaborate the review, hundreds of studies conducted between the 1980s and 2020s were first selected based on their relevance to the scope. Afterward, the most relevant contributions were classified and chronologically sorted. From the classification, it is observed that most reviewed studies were conducted to evaluate the thermal performance of buildings or detect their defects using images collected by an infrared camera. At the same time, a considerable number of studies used thermal images obtained by a satellite to observe the urban heat island effect. Despite the important number of contributions in infrared thermography at multiple scales of the built environment, three main research gaps or opportunities can be identified in the literature. First, it would be possible to perform a more detailed analysis of urban heat fluxes using thermal images collected at multiple scales. Then, thermal images collected by a mounted or handheld infrared camera could be used to create building energy models. Finally, better visualization tools would be developed to monitor a city’s energy use and improve its sustainability if thermal images were integrated into Internet-of-Things and digital twin platforms.

Thermal and Circulatory Changes in Diverse Body Regions in Dogs and Cats Evaluated by Infrared Thermography

Abstract: Simple Summary Infrared thermography is a tool that measures changes in the surface temperature of the skin and has been used in companion animals to determine their health state and to diagnose inflammatory processes, neoplasia, pain, or neuropathies. Body regions such as the face, body, or hind/forelimbs are commonly used for thermography in dogs and cats. However, since there is disagreement about the differences in temperature recorded using this tool, this article analyzes the usefulness of IRT in companion animals (pets) as a complementary diagnostic method for evaluating thermal and circulatory changes. It analyzes the recent scientific evidence on the use of facial, body, and appendicular thermal windows in dogs and cats under different clinical conditions. Abstract Infrared thermography (IRT) has been proposed as a method for clinical research to detect local inflammatory processes, wounds, neoplasms, pain, and neuropathies. However, evidence of the effectiveness of the thermal windows used in dogs and cats is discrepant. This review aims to analyze and discuss the usefulness of IRT in diverse body regions in household animals (pets) related to recent scientific evidence on the use of the facial, body, and appendicular thermal windows. IRT is a diagnostic method that evaluates thermal and circulatory changes under different clinical conditions. For the face, structures such as the lacrimal caruncle, ocular area, and pinna are sensitive to assessments of stress degrees, but only the ocular window has been validated in felines. The usefulness of body and appendicular thermal windows has not been conclusively demonstrated because evidence indicates that biological and environmental factors may strongly influence thermal responses in those body regions. The above has led to proposals to evaluate specific muscles that receive high circulation, such as the biceps femoris and gracilis. The neck area, perivulvar, and perianal regions may also prove to be useful thermal windows, but their degree of statistical reliability must be established. In conclusion, IRT is a non-invasive technique that can be used to diagnose inflammatory and neoplastic conditions early. However, additional research is required to establish the sensitivity and specificity of these thermal windows and validate their clinical use in dogs and cats.

Quantitative imaging of printed circuit board (PCB) delamination defects using laser-induced ultrasound scanning imaging

Abstract: Laser-induced ultrasound scanning imaging is proposed and utilized for the detection of the printed circuit board (PCB) delamination defect in this present study. Initially, based on the principle of laser-induced ultrasound scanning imaging, a three-dimensional mathematical model of the ultrasonic excitation by pulsed laser acting on the surface of PCB is established and analyzed. Furthermore, based on the established laser ultrasonic nondestructive testing system, single-point testing is investigated on the PCB specimen. A-scan experiments were carried out by transmission and reflection approaches, respectively. Moreover, the influence of the signal receiving position on the discrimination of defective signals and the effect of wavelet transform denoising parameters on the signal-to-noise ratio were investigated. Eventually, based on the laser-induced ultrasound scanning imaging inspection system, the defects of simulated debonding flat bottom holes are detected and studied. The different algorithms or parameters (Fast Fourier Transform, variance, extremum, and principal component analysis, etc.) are employed to extract the characteristic information are analyzed. The experimental results are compared with the traditional infrared thermal wave imaging (lock-in thermography). The experimental results indicate that laser-induced ultrasound scanning imaging has the advantages of high-resolution imaging for the defect with a small diameter. Therefore, it is of great significance to study a set of feasible laser-induced ultrasound scanning imaging for PCB delamination defect detection.

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Abstract: In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

Introduction of the combination of thermal fundamentals and Deep Learning for the automatic thermographic inspection of thermal bridges and water-related problems in infrastructures

Abstract: Infrastructure inspection is fundamental to keep its service performance at the highest level. For that, special attention should be paid to the most severe defects in order to be able to subsequently mitigate or even eliminate them. Therefore, this paper introduces the combination of an automatic thermogram pre-processing algorithm and a Deep Learning (DL) model, Mask R-CNN, applied to thermal images acquired from different infrastructures (buildings, heritage sites and civil infrastructures) with water-related problems and thermal bridges. The pre-processing algorithm developed is based on thermal fundamentals. As an output, the thermal contrast between defect and defect-free areas is increased in each image. Then, Mask R-CNN is trained using the pre-processing algorithm outputs as input dataset to automatically detect, segment and classify each defect area. The training process of Mask R-CNN is improved by the prior application of the proposed pre-processing algorithm in terms of time. This shows the capacity of thermal fundamentals to improve the performance of the DL models for their application to the InfraRed Thermography (IRT) field. In addition, DL models are introduced for the first time in the thermographic inspection of water-related problems and thermal bridges when inspecting an infrastructure.

Detection of Breast Cancer from Five-View Thermal Images Using Convolutional Neural Networks

Abstract: Breast cancer is one of the most common forms of cancer. Its aggressive nature coupled with high mortality rates makes this cancer life-threatening; hence early detection gives the patient a greater chance of survival. Currently, the preferred diagnosis method is mammography. However, mammography is expensive and exposes the patient to radiation. A cost-effective and less invasive method known as thermography is gaining popularity. Bearing this in mind, the work aims to initially create machine learning models based on convolutional neural networks using multiple thermal views of the breast to detect breast cancer using the Visual DMR dataset. The performances of these models are then verified with the clinical data. Findings indicate that the addition of clinical data decisions to the model helped increase its performance. After building and testing two models with different architectures, the model used the same architecture for all three views performed best. It performed with an accuracy of 85.4%, which increased to 93.8% after the clinical data decision was added. After the addition of clinical data decisions, the model was able to classify more patients correctly with a specificity of 96.7% and sensitivity of 88.9% when considering sick patients as the positive class. Currently, thermography is among the lesser-known diagnosis methods with only one public dataset. We hope our work will divert more attention to this area.

UAV-Based Photogrammetry and Infrared Thermography Applied to Rock Mass Survey for Geomechanical Purposes

Abstract: A research study aimed at the extending the means of estimating ISRM (International Society for Rock Mechanics) geomechanical parameters through non-contact methodologies, in the frame of the remote survey of rock masses, is herein presented. It was conducted by coupling UAV-based photogrammetry and Infrared Thermography. Starting from georeferenced UAV surveys and the definition of rock masses’ RGB point clouds, different approaches for the extraction of discontinuity spatial data were herein compared according to the ISRM subjective and objective discontinuity sampling criteria. These were applied to a survey a window and along a scanline, both defined on the dense point clouds, to simulate a field rock mass survey, although carried out on remotely acquired data. Spatial discontinuity data were integrated via the analysis of dense point clouds built from IRT images, which represents a relatively new practice in remote sensing, and the processing of thermograms. Such procedures allowed the qualitative evaluation of the main geomechanical parameters of tested rock masses, such as aperture, persistence and weathering. Moreover, the novel parameters of Thermal-spacing (T-spacing) and Thermal-RQD (T-RQD) are herein introduced in a tentative attempt at extending the application field of IRT to remote rock mass surveys for practical purposes. The achieved results were validated by field campaign, demonstrating that a remote survey of rock masses can be conducted according to the ISRM procedures even on models built by integrating RGB and IRT photogrammetry. In fact, these two technologies are positively complementary and, besides being feasible, are characterized by a relatively quick and non-contact execution. Thanks to the positive and satisfactory results achieved herein, this research contributes to the implementation of the scientific and technical casuistry on the remote survey of rock masses, which is a technical field offering a wide range of applications.

Neonatal infrared thermography images in the hypothermic ruminant model: Anatomical-morphological-physiological aspects and mechanisms for thermoregulation

Abstract: Hypothermia is one factor associated with mortality in newborn ruminants due to the drastic temperature change upon exposure to the extrauterine environment in the first hours after birth. Ruminants are precocial whose mechanisms for generating heat or preventing heat loss involve genetic characteristics, the degree of neurodevelopment at birth and environmental aspects. These elements combine to form a more efficient mechanism than those found in altricial species. Although the degree of neurodevelopment is an important advantage for these species, their greater mobility helps them to search for the udder and consume colostrum after birth. However, anatomical differences such as the distribution of adipose tissue or the presence of type II muscle fibers could lead to the understanding that these species use their energy resources more efficiently for heat production. The introduction of unconventional ruminant species, such as the water buffalo, has led to rethinking other characteristics like the skin thickness or the coat type that could intervene in the thermoregulation capacity of the newborn. Implementing tools to analyze species-specific characteristics that help prevent a critical decline in temperature is deemed a fundamental strategy for avoiding the adverse effects of a compromised thermoregulatory function. Although thermography is a non-invasive method to assess superficial temperature in several non-human animal species, in newborn ruminants there is limited information about its application, making it necessary to discuss the usefulness of this tool. This review aims to analyze the effects of hypothermia in newborn ruminants, their thermoregulation mechanisms that compensate for this condition, and the application of infrared thermography (IRT) to identify cases with hypothermia.

On the Machining Temperature and Hole Quality of CFRP Laminates when Using Diamond-Coated Special Drills

Abstract: Carbon fiber reinforced polymers (CFRPs) are attractive engineering materials in the modern aerospace industry, but possess extremely poor machinability because of their inherent anisotropy and heterogeneity. Although substantial research work has been conducted to understand the drilling behavior of CFRPs, some critical aspects related to the machining temperature development and its correlations with the process parameters still need to be addressed. The present paper aims to characterize the temperature variation and evolution during the CFRP drilling using diamond-coated candlestick and step tools. Progression of the composite drilling temperatures was recorded using an infrared thermography camera, and the hole quality was assessed in terms of surface morphologies and hole diameters. The results indicate that the maximum drilling temperature tends to be reached when the drill edges are fully engaged into the composite workpiece. Then it drops sharply as the tool tends to exit the last fiber plies. Lower cutting speeds and lower feed rates are found to favor the reduction of the maximum composite drilling temperature, thus reducing the risk of the matrix glass transition. The candlestick drill promotes lower magnitudes of drilling temperatures, while the step drill yields better surface morphologies and more consistent hole diameters due to the reaming effects of its secondary step edges.

Intra- and Early Postoperative Evaluation of Malperfused Areas in an Irradiated Random Pattern Skin Flap Model Using Indocyanine Green Angiography and Near-Infrared Reflectance-Based Imaging and Infrared Thermography

Abstract: Background: Assessment of tissue perfusion after irradiation of random pattern flaps still remains a challenge. Methods: Twenty-five rats received harvesting of bilateral random pattern fasciocutaneous flaps. Group 1 served as nonirradiated control group. The right flaps of the groups 2–5 were irradiated with 20 Gy postoperatively (group 2), 3 × 12 Gy postoperatively (group 3), 20 Gy preoperatively (group 4) and 3 × 12 Gy preoperatively (group 5). Imaging with infrared thermography, indocyanine green angiography and near-infrared reflectance-based imaging were performed to detect necrotic areas of the flaps. Results: Analysis of the percentage of the necrotic area of the irradiated flaps showed a statistically significant increase from day 1 to 14 only in group 5 (p < 0.05). Indocyanine green angiography showed no differences (p > 0.05) of the percentage of the nonperfused area between all days in group 1 and 3, but a decrease in group 2 in both the left and the right flaps. Infrared thermography and near-infrared reflectance-based imaging did not show evaluable differences. Conclusion: Indocyanine green angiography is more precise in prediction of necrotic areas in random pattern skin flaps when compared to hyperspectral imaging, thermography or clinical impression. Preoperative fractional irradiation with a lower individual dose but a higher total dose has a more negative impact on flap perfusion compared to higher single stage irradiation.

Wireless graphene-based thermal patch for obtaining temperature distribution and performing thermography

Abstract: Thermal imaging provides information regarding the general condition of the human body and facilitates the diagnosis of various diseases. Heat therapy or thermotherapy can help in the treatment of injuries to the skin tissue. Here, we report a wearable thermal patch with dual functions of continuous skin temperature sensing and thermotherapy for effective self-care treatment. This system consists of a graphene-based capacitive sensor, a graphene thermal pad, and a flexible readout board with a wireless communication module. The wearable sensor continuously monitors the temperature variation over a large area of the skin (3 × 3cm2) with high resolution and sensitivity and performs thermotherapy via the graphene-based heater mounted at the bottom of the device. Animal studies prove that the proposed system can be used to diagnose various diseases. This technology could be useful in the development of convenient and wearable health care devices.

Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning

Abstract: ABSTRACT The occurrence of multiple faults is a practical problem in the bearings of rotating machines, and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0. The present work investigated various combinations of bearing faults, including dual and multiple fault conditions. Two prevalent fault diagnosis methods were employed: vibration monitoring using time-frequency scalograms extracted through Continuous Wavelet Transform (CWT) and a non-invasive Infrared Thermography (IRT). A 2-D Convolutional Neural Network (CNN) was used to classify various combinations of fault conditions through automated feature extraction. The proposed methodology was validated at two constant speed conditions of 19 Hz and 29 Hz and continuously accelerated and decelerated speed conditions in the range of 19 Hz - 29 Hz. Adequate accuracy was achieved in both dual and multiple fault conditions in the case of vibration-based fault diagnosis, with a range of 99.39 % to 99.97 %. Meanwhile, in the case of proposed IRT-based fault diagnosis, 100 % classification accuracy was achieved for dual and multiple faults in all conditions. These results signify the robustness and reliability of the proposed methodology for dual and multiple fault diagnosis in bearings at constant and varying speed conditions.

Infrared Thermography in Wound Care, Surgery, and Sports Medicine: A Review

Abstract: For many years, the role of thermometry was limited to systemic (core body temperature) measurements (e.g., pulmonary catheter) or its approximation using skin/mucosa (e.g., axillary, oral, or rectal) temperature measurements. With recent advances in material science and technology, thermal measurements went beyond core body temperature measurements and found their way in many medical specialties. The article consists of two primary parts. In the first part we overviewed current clinical thermal measurement technologies across two dimensions: (a) direct vs. indirect and (b) single-point vs. multiple-point temperature measurements. In the second part, we focus primarily on clinical applications in wound care, surgery, and sports medicine. The primary focus here is the thermographic imaging modality. However, other thermal modalities are included where relevant for these clinical applications. The literature review identified two primary use scenarios for thermographic imaging: inflammation-based and perfusion-based. These scenarios rely on local (topical) temperature measurements, which are different from systemic (core body temperature) measurements. Quantifying these types of diseases benefits from thermographic imaging of an area in contrast to single-point measurements. The wide adoption of the technology would be accelerated by larger studies supporting the clinical utility of thermography.

Adversarial Regressive Domain Adaptation Approach for Infrared Thermography-Based Unsupervised Remaining Useful Life Prediction

Abstract: Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad concern in solving domain shifts. However, simultaneously aligning marginal and conditional distributions in cross-domain remaining useful life (RUL) prediction is rarely researched in adversarial-based DA. In this article, an adversarial regressive domain adaptation (ARDA) approach is, thus, put forward to address this challenge. First, a regressive disparity discrepancy is designed to describe the dissimilarity between distributions and derive the generalization bound for cross-domain prognostics. Guided by this bound, the ARDA effectively aligns marginal and conditional distributions by learning indistinguishable features and considering the relationship between samples and prediction tasks. Simulated and experimental infrared degradation image datasets are used to demonstrate the effectiveness and superiority of the proposed approach over existing methods for cross-domain RUL prediction.

Inline Defective Laser Weld Identification by Processing Thermal Image Sequences with Machine and Deep Learning Techniques

Abstract: The non-destructive testing methods offer great benefit in detecting and classifying the weld defects. Among these, infrared (IR) thermography stands out in the inspection, characterization, and analysis of the defects from the camera image sequences, particularly with the recent advent of deep learning. However, in IR, the defect classification becomes a cumbersome task because of the exposure to the inconsistent and unbalanced heat source, which requires additional supervision. In light of this, authors present a fully automated system capable of detecting defective welds according to the electrical resistance properties in the inline mode. The welding process is captured by an IR camera that generates a video sequence. A set of features extracted by such video feeds supervised machine learning and deep learning algorithms in order to build an industrial diagnostic framework for weld defect detection. The experimental study validates the aptitude of a customized convolutional neural network architecture to classify the malfunctioning weld joints with mean accuracy of 99% and median f1 score of 73% across five-fold cross validation on our locally acquired real world dataset. The outcome encourages the integration of thermographic-based quality control frameworks in all applications where fast and accurate recognition and safety assurance are crucial industrial requirements across the production line.

Infrared-based visualization of exhalation flows while wearing protective face masks

Abstract: Since the onset of the COVID-19 pandemic, a large number of flow visualization procedures have been proposed to assess the effect of personal protective equipment on respiratory flows. This study suggests infrared thermography as a beneficial visualization technique because it is completely noninvasive and safe and, thus, can be used on live individuals rather than mannequins or lung simulators. Here, we examine the effect of wearing either of three popular face coverings (a surgical mask, a cloth mask, or an N95 respirator with an exhalation valve) on thermal signatures of exhaled airflows near a human face while coughing, talking, or breathing. The flow visualization using a mid-wave infrared camera captures the dynamics of thermal inhomogeneities induced by increased concentrations of carbon dioxide in the exhaled air. Thermal images demonstrate that both surgical and cloth face masks allow air leakage through the edges and the fabric itself, but they decrease the initial forward velocity of a cough jet by a factor of four. The N95 respirator, on the other hand, reduces the infrared emission of carbon dioxide near the person's face almost completely. This confirms that the N95-type mask may indeed lead to excessive inhalation of carbon dioxide as suggested by some recent studies.

Comparative analysis of infrared thermography processing techniques for roadways’ sub-pavement voids detection

Abstract: Roadways' sub-pavement voids caused by eroded soil through damaged culverts lead to safety hazards, traffic inconvenience, and expensive repairs. Infrared thermography (IRT) could help to identify those voids before the structural integrity of roadway pavements is compromised. However, IRT suffers from poor signal-to-noise ratio. This study implements the use of three advanced image-processing techniques to increase the accuracy of IRT in detecting voids underneath a roadway. A comparison between R2-based analysis, principal component thermography (PCT), and sparse principal component thermography (S-PCT) is presented and validated through extensive tests on a laboratory-scale section of a roadway. Results show pros and cons of the three techniques and how S-PCT allows determining the physical size of sub-pavement voids with an accuracy above 95%. This research provides the foundation for comparing advanced image-processing techniques that can progress the use of IRT as a more accurate and cost-effective nondestructive evaluation method for roadways’ condition monitoring.

A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations

Abstract: Condition-based monitoring (CBM) has emerged as a critical instrument for lowering the cost of unplanned operations while also improving the efficacy, execution, and dependability of tools. Thermal abnormalities can be thoroughly examined using thermography for condition monitoring. Thanks to the advent of high-resolution infrared cameras, researchers are paying more attention to thermography as a non-contact approach for monitoring the temperature rise of objects and as a technique in great experiments to analyze processes thermally. It also allows for the early identification of weaknesses and failures in equipment while it is in use, decreasing system downtime, catastrophic failure, and maintenance expenses. In many applications, the usage of IRT as a condition monitoring approach has steadily increased during the previous three decades. Infrared cameras are steadily finding use in research and development, in addition to their routine use in condition monitoring and preventative maintenance. This study focuses on infrared crucial thermographic theoretical stages, experimental methodologies, relative and absolute temperature requirements, and infrared essential thermographic theoretical processes for electrical and electronics energy applications. Furthermore, this article addresses the major concerns and obstacles and makes some specific recommendations for future development. With developments in artificial intelligence, particularly computer fiction, depending on the present deep learning algorithm, IRT can boost CBM analysis.