Search Results (87)

Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions. Full article

This study presents the results of over thirty years of non-destructive testing (NDT) in a historic church, providing an unprecedented time analysis of the structural and material integrity of the building and its works of art. During this time, the church has undergone several restorations and two major seismic events. The diagnostics, which include a calibrated mix of established and advanced micro-destructive and non-destructive (NDT) techniques such as X-ray fluorescence, holographic interferometry, electronic speckle pattern interferometry (ESPI), infrared thermography, and IR reflectography, provide critical insights into the impact of the restoration interventions and the earthquakes on the church’s artistic heritage. The results indicate varying degrees of effectiveness of the restoration efforts, highlighting both areas of successful conservation and emerging vulnerabilities. This long-term study highlights the importance of continuous monitoring and its integration with NDT in identifying the effects of time and strong events occurring during the life of artworks that influence their state of conservation. Full article

Fiber-to-fiber recycling must be promoted to achieve sustainability in the textile industry. Mixed fiber materials cause significant issues in recycling, making accurate sorting essential for recycling. Garments may contain internal materials called interlinings, which are wrapped in the outer fabric and are not visible from the garment’s surface. This study proposes a non-destructive method for detecting interlinings in garments using active infrared (IR) thermography. Numerical simulations showed that the presence, thickness, and material of the interlining affected the cooling behavior. Fourier analysis of the surface temperature curves revealed that an increased interlining thickness leads to slower cooling and a greater phase lag, enabling the identification of interlining characteristics from thermal responses. Full article

Breast cancer remains a leading cause of cancer-related deaths among women worldwide, highlighting the urgent need for early detection. While mammography is the gold standard, it faces cost and accessibility barriers in resource-limited areas. Infrared thermography is a promising cost-effective, non-invasive, painless, and radiation-free alternative that detects tumors by measuring their thermal signatures through thermal infrared radiation. However, challenges persist, including limited clinical validation, lack of Food and Drug Administration (FDA) approval as a primary screening tool, physiological variations among individuals, differing interpretation standards, and a shortage of specialized radiologists. This survey uniquely focuses on integrating texture analysis and machine learning within infrared thermography for breast cancer detection, addressing the existing literature gaps, and noting that this approach achieves high-ranking results. It comprehensively reviews the entire processing pipeline, from image preprocessing and feature extraction to classification and performance assessment. The survey critically analyzes the current limitations, including over-reliance on limited datasets like DMR-IR. By exploring recent advancements, this work aims to reduce radiologists’ workload, enhance diagnostic accuracy, and identify key future research directions in this evolving field. Full article

The assessment of ocular surface temperature (OST) plays a pivotal role in the diagnosis and management of various ocular diseases. This paper introduces significant enhancements to the ThermOcular system, initially developed for precise OST measurement using infrared (IR) thermography. These advancements focus on accuracy improvements that reduce user dependency and increase the system’s diagnostic capabilities. A novel addition to the system includes the use of EyeTags, which assist clinicians in selecting control points more easily, thus reducing errors associated with manual selection. Furthermore, the integration of state-of-the-art semantic segmentation models trained on the newest dataset is explored. Among these, the OCRNet-HRNet-w18 model achieved a segmentation accuracy of 96.21% MIOU, highlighting the effectiveness of the improved pipeline. Additionally, the challenge of eliminating eyelashes in IR frames, which cause artifactual measurement errors in OST assessments, is addressed. Through a newly developed method, the influence of eyelashes is eliminated, thereby enhancing the precision of temperature readings. Moreover, an algorithm for blink detection and elimination is implemented, significantly improving upon the basic methods previously utilized. These innovations not only enhance the reliability of OST measurements, but also contribute to the system’s efficiency and diagnostic accuracy, marking a significant step forward in ocular health monitoring and diagnostics. Full article

Converting carbon dioxide (CO₂) into high-value fuels through the photothermal effect offers an effective approach to enhancing the carbon cycle and reducing the greenhouse effect. In this study, we developed Ag/C-TCN-x, a carbon nitride-based photocatalyst that integrates both photothermal and localized surface plasmon resonance (LSPR) effects. This material was synthesized through a three-step process involving hydrothermal treatment, calcination, and photo-deposition. Real-time infrared thermography monitoring revealed that Ag/C-TCN-2 reached a surface stabilization temperature of approximately 176 °C, which was 1.5 times higher than C-TCN and 2.2 times higher than g-C₃N₄. Under the same experimental conditions, Ag/C-TCN demonstrated a carbon monoxide (CO) release rate 3.3 times greater than that of pure g-C₃N₄. The composite sample Ag/C-TCN-2 maintained good photocatalytic activity in five cycling tests. The structural stability of the sample after the cycling tests was confirmed by X-ray diffraction (XRD) test. The unique tubular structure of Ag/C-TCN increased its specific surface area, facilitating enhanced CO₂ adsorption. Carbon doping not only triggered the photothermal effect but also accelerated the conversion of carriers. Additionally, the LSPR effect of Ag nanoparticles, combined with carbon doping, optimized charge carrier dynamics and promoted efficient CO₂ photoreduction. The CO₂ reduction mechanism over Ag/C-TCN was further examined using in situ Fourier Transform Infrared (FT-IR) spectroscopy. This research offers valuable insights into how photothermal and LSPR effects can be harnessed to enhance the efficiency of CO₂ photoreduction. Full article

Concrete, known for its high strength, durability, and flexibility, is a core material in construction. However, defects such as voids and honeycombing often occur due to improper pouring or vibration, weakening the concrete’s strength and affecting its long-term performance. These defects typically require costly repairs. Therefore, timely identification and repair of such early defects is crucial for improving construction quality. This paper proposes a method for non-destructive detection of honeycomb defects in concrete using infrared thermography (IR) during the hydration stage. By analyzing the temperature differences between defect and non-defect areas based on the temperature distribution generated during hydration, defects can be detected. Furthermore, the study uses the COMSOL finite element model to explore the relationship between defect size, ambient temperature, formwork thickness, and thermal contrast. The results show that IR technology can effectively and reliably detect honeycomb defects, especially during the hydration phase. As a convenient and feasible non-destructive testing method, IR technology has significant potential for application and development in concrete defect detection. Full article

AISI 316 is a stainless steel known for its exceptional corrosion resistance and excellent mechanical properties. It is used in the chemical and pharmaceutical industries, food processing equipment, and medical devices. This alloy’s wide range of applications underscores its importance in industries requiring materials that can withstand extreme conditions while maintaining structural integrity and performance. Additionally, the excellent weldability and formability of AISI 316 allow for versatile design and production processes, ensuring durable and reliable performance in marine environments. This work aims to examine the behavior of AISI 316L and its welded joints under high-cycle fatigue loadings using infrared thermography (IR). Two kinds of experimental tests are performed on specimens with the same geometry: static tests and stepwise succession tests. The results of the static tests are in accordance with the stepwise succession test results in predicting the fatigue properties. Full article

Hydrogen leakage in Proton Exchange Membrane (PEM) fuel cells poses critical safety, efficiency, and operational reliability risks. This study introduces an innovative infrared (IR) thermography-based methodology for detecting and quantifying hydrogen leaks towards the outside of PEM fuel cells. The proposed method leverages the catalytic properties of a membrane electrode assembly (MEA) as an active thermal tracer, facilitating real-time visualisation and assessment of hydrogen leaks. Experimental tests were conducted on a single-cell PEM fuel cell equipped with intact and defective gaskets to evaluate the method’s effectiveness. Results indicate that the active tracer generates distinct thermal signatures proportional to the leakage rate, overcoming the limitations of hydrogen’s low IR emissivity. Comparative analysis with passive tracers and baseline configurations highlights the active tracer-based approach’s superior positional accuracy and sensitivity. Additionally, the method aligns detected thermal anomalies with defect locations, validated through pressure distribution maps. This novel, non-invasive technique offers precise, reliable, and scalable solutions for hydrogen leak detection, making it suitable for dynamic operational environments and industrial applications. The findings significantly advance hydrogen’s safety diagnostics, supporting the broader adoption of hydrogen-based energy systems. Full article

Infrared (IR) magneto-optical (MO) bi-imaging is an innovative method for detecting weld defects, and it is important to process both IR thermography and MO imaging characteristics of weld defects. IR thermography and MO imaging can not only run simultaneously but can also run separately in special welding processes. This paper studies the sensing processing of eddy current IR thermography and MO imaging for detecting weld defects of laser spot welding and butt joint laser welding, respectively. To address the issues of high-level noise and low contrast in eddy current IR detection thermal images interfering with defect detection and recognition, a method based on least squares and Gaussian-adaptive bilateral filtering is proposed for denoising eddy current IR detection thermal images of laser spot welding cracks and improving the quality of eddy current IR detection thermal images. Meanwhile, the image gradient is processed by Gaussian-adaptive bilateral filtering, and then the filter is embedded in the least squares model to smooth and denoise the image while preserving defect information. Additionally, MO imaging for butt joint laser welding defects is researched. For the acquired MO images of welding cracks, pits, incomplete fusions, burn-outs, and weld bumps, the MO image processing method that includes median filtering, histogram equalization, and Wiener filtering was used, which could eliminate the noise in an image, enhance its contrast, and highlight the weld defect features. The experimental results show that the proposed image processing method can eliminate most of the noise while retaining the weld defect features, and the contrast between the welding defect area and the normal area is greatly improved. The denoising effect using the Natural Image Quality Evaluator (NIQE) and the Blind Image Quality Index (BIQI) has been evaluated, further demonstrating the effectiveness of the proposed method. The differences among weld defects could be obtained by analyzing the gray values of the weld defect MO images, which reflect the weld defect information. The MO imaging method can be used to investigate the magnetic distribution characteristics of welding defects, and its effectiveness has been verified by detecting various butt joint laser welding weldments. Full article

In this paper, the thermal insulation performance of a wooden house was evaluated with infrared thermographies which were captured by a non-contact and non-destructive method. Heat transmissions were determined by the difference between surface temperature of outdoor and indoor sides of the walls, which were measured with an IR ray signal, and indoor and outdoor air temperatures. The heat transmission coefficient, which was determined by IR thermography, was compared to the coefficient calculated with thermal conductivities of wall component materials. The heat transmission coefficient calculated through wall components was 0.24 W/m²·K, while the coefficients determined with IR thermography ranged from 0.27 to 4.61 W/m²·K. The invisible thermal insulation defects in the wall, such as heat losses from the premature deterioration of thermal insulation material and air leakage through windows, were observed by IR thermography. It is expected that the results of this study could be used effectively not only for improving thermal insulation performance but also for suppressing decay occurrence in wooden building materials. Full article

Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases of dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early detection by capturing subtle thermal variations indicative of breast abnormalities. This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. To maximize detection accuracy, data preprocessing, feature extraction, and dimensionality reduction were implemented to isolate distinguishing characteristics across tissue types. Leveraging advanced feature extraction and visualization techniques inspired by geospatial data methodologies, we evaluated several deep learning architectures and classical classifiers using the DRM-IR and Breast Thermography Mendeley thermal datasets. Among the tested models, the ResNet152 architecture combined with a Support Vector Machine (SVM) classifier delivered the highest performance, achieving 97.62% accuracy, 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score of 97.16%, an area under the curve (AUC) of 99%, a latency of 0.06 s, and CPU utilization of 88.66%. These findings underscore the potential of integrating infrared thermography with advanced deep learning and machine learning approaches to significantly improve the accuracy and efficiency of breast cancer detection, supporting its role as a valuable tool for early diagnosis. Full article

This article presents the application of infrared thermography as a nondestructive testing method (NDT) for detecting osmotic damage in glass-fiber-reinforced polymer (GFRP) and glass-reinforced polymer (GRP) boat hull structures. The aim of the conducted experiments is to explore the possibilities of applying active infrared thermography to real structures and to establish a procedure capable of filtering out anomalies caused by various thermal influences, such as thermal reflections from surrounding objects, geometry effects, and heat flow variations on the observed object. The methods used for post-processing IR signals include lock-in thermography (LT), pulse thermography (PT), pulse phase thermography (PPT), and gradient pulse phase thermography (GT). The practical application and advantages and disadvantages of infrared thermography in identifying osmotic damage in GFRP and GRP boat hulls will be illustrated through three case studies. Each case study is based on specific conditions and characteristics of different types of osmotic damage, enabling a thorough analysis of the effectiveness of the method in detecting and assessing the severity of the damage. The post-processed thermal images enable a clearer distinction between damaged and undamaged zones, improving the robustness of detection under realistic field conditions. Full article

Firefighters need personal protection equipment and protective clothing to be safe and protected when responding to fire incidents. At present, firefighters’ suits are developed by using inherently thermal-resistant fibers but pose serious problems related to comfort. In the present research, multilayered fire-fighting fabrics were developed with different fiber blends. Multilayer fire retardant (FR) fabrics with phase change materials (PCMs) inserts were developed and compared with reference multilayer fabrics without PCM. In this context, four fabric samples were chosen to fabricate the multilayer FR fabrics. Properties of multilayer fabrics were investigated, which include physical, thermo–physiological comfort, and flame-resistant performance. The heating process of the clothing was examined using infrared (IR) thermography, differential scanning calorimetry (DSC), thermal protective testing (TPP), and steady-state (Convective and Radiant) heat resistance tests. Areal density and thickness were measured as physical parameters, and air permeability (AP), overall moisture management capacity (OMMC), and thermal conductivity were measured as thermo–physiological comfort characteristics. The inclusion of PCM improved the thermal protection as well as flame resistance significantly. Sample S1 (Nomex + PTFE + Nomex with PCM) demonstrated superior fire resistance, air permeability, and thermal protection, with a 37.3% increase in air permeability as compared to the control sample (SC) by maintaining comfort while offering high thermal resilience. The inclusion of PCM enhanced its thermal regulation, moderating heat transfer. Flame resistance tests confirmed its excellent performance, while thermo–physiological assessments highlighted a well-balanced combination of thermal conductivity and air permeability. This study will help to improve the performance of firefighter protective fabrics and provide guidelines in terms of balancing comfort and performance while designing firefighter protective clothing for different climatic conditions. Full article

This paper deals with the experimental procedures of lock-in thermography (LIT) for polyetheretherketone (PEEK), which is used as a lightweight material in various industrial fields. The LIT has limitations due to non-uniform heating by external optic sources and the non-uniformity correction (NUC) of the infrared (IR) camera. It is generating unintended contrast in the IR image in thermal imaging inspection, reducing detection performance. In this study, the non-uniformity effect was primarily improved by producing an equivalent array halogen lamp. Then, we presented absolute temperature compensation (ATC) and temperature ratio compensation (TRC) techniques, which can equalize the thermal contrast of the test samples by compensating for them using reference samples. By applying compensation techniques to data acquired from the test samples, defect detectability improvement was quantitatively presented. In addition, binarization was performed and detection performance was verified by evaluating the roundness of the detected defects. As a result, the contrast of the IR image was greatly improved by applying the compensation technique. In particular, raw data were enhanced by up to 54% using the ATC compensation technique. Additionally, due to improved contrast, the signal-to-noise ratio (SNR) was improved by 7.93%, and the R² value of the linear trend equation exceeded 0.99, demonstrating improved proportionality between the defect condition and SNR. Full article