Thermographic Data Research Articles

Applications of Independent Component Thermography for Testing and Evaluation of Glass Fibre Reinforced Polymer Materials

Abstract A novel post-processing technique is proposed for analysing time series thermographic data obtained by imposing a frequency-modulated heat flux on a Glass Fiber Reinforced Polymer (GFRP) material for Non-Destructive Testing and Evaluation (NDT&E). The proposed approach bridges the gap between the statistical and spatiotemporal analysis of the captured thermographic sequence to inspect and identify the flat bottom holes in the sample. It emphasizes the defect detection capabilities of the Principal Component Analysis (PCA) based thermography named Principal Component Thermography (PCT) and Independent Component Analysis (ICA) based thermography named Independent Component Thermography (ICT) and compares them by using two distinct algorithmic implementations for each method. The effectiveness of these four algorithmic implementations is evaluated using the dynamic range of the temporal profiles. This work presents a significant step toward gaining deeper insight into statistical post-processing techniques for defect identification in InfraRed Thermography (IRT).

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

Fatigue crack closure assessment by wavelet transform of infrared thermography signals

The occurrence of crack closure significantly impacts the fatigue life of materials and structural components. Whether it is induced by the nature of the loading, the fabrication process or the geometry of the structure, its magnitude and effect should be considered to further improve predictive models of fatigue crack propagation. However, the definition of reliable experimental methods for the observation and assessment of fatigue crack closure, and in particular suited to structure testing, remains a challenge. The present study aims to provide a novel approach for the assessment of fatigue crack closure via the continuous wavelet transform of infrared thermography data. The processing of the temperature signal close to the crack in a coherent time–frequency space allows for the identification of crack closing and opening instants associated with high-frequency components. The method is meant to be suited to any testing configuration (conventional compact tension specimen or full-scale structures) with minimum operator-dependent parameters.

Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model

In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.

Signal reconstruction with infrared thermographic data in the application of rail defect detection

Signal reconstruction with infrared thermographic data in the application of rail defect detection

Assessment of Hydraulic Oil Properties during Operation of a Mini Loader

This study employs a rigorous methodology to assess the condition of the hydraulic oil in a recently introduced mini skid-steer loader. The assessment is conducted through laboratory analysis, which adheres to a range of international standards. The objective is to provide accurate insights into the viscosity, particle count, and characterisation of the oil, along with thermographic data. The friction of oil is evaluated at specific time points: 0, 10, 100, 125, and 150 h of operation. This examination offers a comprehensive insight into the alterations in oil characteristics during a pivotal period when machine components are undergoing initial consolidation and abrasion to attain the factory-defined performance thresholds. The principal aim of this research is to provide valuable insights into the wear of oil and hydraulic system components through an in-depth analysis of a range of variables. Moreover, the investigation aims to ascertain the impact of this factor on the temperature elevation of system components and accessories to formulate enhanced technical guidelines for implementation. The main results indicate the presence of particles in the oil, resulting in a cleanliness code of 23/21/13, which exceeds the permissible threshold of 20/18/15 specified in ISO 11171. In addition, hydraulic oil shows a viscosity instability of more than 10% due to moisture absorption, leading to wear of mechanical components composed of iron, nickel, copper, zinc, and silicon. This deterioration is corroborated by thermographic evaluations, which reveal a considerable temperature increase in components such as cylinders and system accessories.

Automated CFRP impact damage detection with statistical thermographic data and machine learning

The study is focused on the use of machine learning models for the automated detection of impact damage in carbon fiber reinforced polymer (CFRP) by flash-pulse thermographic testing. A new method for thermographic data pre-processing, which is based on statistical features, was proposed. Nine machine learning models for the automated detection of impact damage in CFRP samples were applied to the raw thermographic data, data pre-processed by the suggested method and data pre-processed by the widely used thermographic signal reconstruction (TSR) method. The machine learning models were tested to provide a binary classification of impact damage in CFRP. The results presented in this study show improved performance of the classification if the data are pre-processed by the proposed method. The best results were obtained by a Bagged tree ensemble trained with statistical features. The final balanced accuracy achieved for the Bagged trees model trained on 40 statistical features was 99.8 % which indicates a very good performance.

Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling

The paper describes the implementation of 3D numerical simulation in machine learning models used in infrared thermographic nondestructive testing. The enhancement of generalizability of such models emerges as a decisive factor for producing trust-worthy test results. First, it is demonstrated that the models trained on datasets with fixed parameters yield limited defect detection capabilities. The concept of training datasets, which include subtle variations in material thickness, thermal conductivity, as well as various combinations of material density and heat capacity, provides the best learning results and a noticeable ability to identify defects in all test datasets. Second, the model robustness in respect to noise is explored to demonstrate its ability to withstand additive and multiplicative random noise. Third, potentials of some known techniques of thermographic data processing, such as Thermographic Signal Reconstruction, Fast Fourier Transform and Temperature Contrast, are examined. In particular, the use of the Temperature Contrast data ensured sensitivity (True Positive Rate) better than 98% across all test datasets.

Efficient defect reconstruction from temporal non-uniform pulsed thermography data using the virtual wave concept

In this study, we present an extension of the virtual wave concept to enable photothermal reconstruction from temporal non-uniform pulsed thermography data. Therefore, we introduce a generalized discrete transformation kernel, which allows to account for arbitrary temporal sampling strategies. First, we show the evidence of the proposed strategy for analytical temperature signals. Moreover, we demonstrate the advantages of the strategy for simulated temperature signals, obtained from an orthotropic sample with defect interfaces at various depth positions. For experimental verification, we apply pulsed thermography in the pulse-echo configuration for a carbon fiber-reinforced polymer sample with different embedded defects. It can be shown that efficient time sampling in the virtual wave concept allows a significant reduction in the number of data points compared to uniform sampling, without compromising the quality of the reconstruction results.

Divertor heat load estimates on NSTX and DIII-D using new and open-source 2D inversion analysis code

A thermography inversion algorithm has been developed in the open-source Python-based computer code, HYPERION, to calculate the heat flux incident on plasma-facing components (PFCs) in axisymmetric tokamaks. The chosen mesh size at the surface significantly affects the calculated transient heat flux results. The calculated transient heat flux will exceed the real value when the mesh size tends to zero but will underestimate the real value when the mesh size is large. A criterion for determining the appropriate mesh size for the transient heat flux calculation will be discussed. The numerical scheme for HYPERION uses a 2D fully implicit finite-difference approach, allowing temperature-dependent thermal properties of PFC materials. The inversion algorithm is benchmarked against established heat flux calculation codes, TACO and THEODOR, based on thermography data from NSTX and DIII-D respectively. The primary benefits of HYPERION compared to TACO and THEODOR are that it is open-source and it allows for the optimization of mesh thickness along the substrate. The algorithm also accounts for the thermal properties of thin surface layers that characteristically form on PFCs due to plasma-material interactions. The agreement between HYPERION and THEODOR is excellent, as the percent difference between the codes is ∼5% on average in the case of the DIII-D data for moderate to high heat flux. Verification tests with TACO show slightly higher average percent differences of 8% and 12%. In using HYPERION to study filaments in heat flux, the initial results indicate that small ELMs filaments significantly broaden the divertor heat flux, and decrease divertor peak flux. Compared to the inter-ELM, the small ELM filaments decrease the divertor peak surface temperature. With intermittent divertor filaments, the divertor heat flux width is comparable with that found in L-mode.

Protocol description and initial experience in kidney graft perfusion using infrared thermography.

Protocol description for renal perfusion study using thermographic technology and description of the thermographic and clinical behavior of the transplanted kidneys before and after unclamping. Infrared thermographic images of renal grafts are obtained before kidney reperfusion, 10min after and just before closing the surgical wound. Thermographic data is evaluated together with the type of graft and donor, cold ischemia time, hypovascularized areas determined by the surgeon during surgical intervention, alterations in vascular flow in postoperative echo-Doppler, time at the beginning of graft function and serum creatinine monitoring during postoperative follow-up. 17 grafts were studied. The mean temperature of the grafts before reperfusion, 10min after and at the end of the surgery were 18.7°C (SD 6.27), 32.36°C (SD1.47) and 32.07°C (SD1.78) respectively. 4 grafts presented hypoperfused areas after reperfusion. These areas presented a lower temperature compared to the well perfused parenchyma surface using thermographic images. The study of the usefulness and applicability of thermography can allow the development of tools that provide additional objective information on organ perfusion in real time and non-invasive manner. Our protocol and initial results can contribute to provide new evidence. Further analyses should be developed to shed light on the role of this technology.

Two-dimensional Hilbert-Huang transform-based thermographic data processing for non-destructive material defect detection

ABSTRACT Previous research has established the Hilbert-Huang transform (HHT) as a powerful tool for processing and analysing one-dimensional time series signals. Its extension into multi-dimensional Euclidean spaces further demonstrates its versatility. In this study, we leverage a two-dimensional (2D) HHT to enhance non-destructive testing, utilising active infrared thermographic data to improve the detection of material defects. The proposed methodology incorporates a 2D HHT, which synergises multidimensional ensemble empirical mode decomposition with the Riesz transform (RT). This combination is adept at uncovering both global (i.e. intrinsic mode functions) and local (i.e. phase angle and spatial frequency) information. In particular, RT is integrated with a Gaussian filter, which allows for a more thorough analysis of the inherent characteristics of defects through the monogenic signal produced by RT. The experimental application of this method on a mosaic sample, intentionally embedded with defects, has yielded promising results. The approach effectively distinguishes critical defect signals from the surroundings, leading to more accurate and clearer results.

Using Thermal Signature to Evaluate Heat Stress Levels in Laying Hens with a Machine-Learning-Based Classifier.

Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.

The Potential of Infrared Thermography for Early Pregnancy Diagnosis in Nili-Ravi Buffaloes.

This study was designed to explore the potential of infrared thermography (IRT) as an alternate approach for early pregnancy diagnosis in buffaloes. The surface temperature (ST) of different regions (eyes, muzzle, flanks, and vulva) was determined in 27 buffaloes using IRT from the day of artificial insemination (AI; Day 0), and measurement was repeated every fourth day until Day 24 post-AI. From all regions, the ST in each thermograph was recorded at three temperature values (maximum, average, minimum). Pregnancy status was confirmed through ultrasonography on Day 30, and animals were retrospectively grouped as pregnant or non-pregnant for analysis of thermographic data. In pregnant buffaloes, all three values of ST were significantly greater (p ≤ 0.05) for the left flank, while, in the left eye and vulva, only the maximum and average values were significantly greater. By contrast, the maximum ST of the muzzle was significantly lower (p ≤ 0.05) in pregnant buffaloes compared to non-pregnant buffaloes. However, the ST of the right eye and right flank did not show significant temperature variation at any value. These findings suggest that IRT has the potential to identify thermal changes associated with pregnancy in buffaloes at an early stage.

Weld Defect Inspection of Cylindrical Secondary Batteries Based on Active Thermographic Measurement with Thermal Magnification

This paper presents an active thermographic measurement-based inspection tech-nique for detecting weld defects in the battery cap region of cylindrical secondary batteries. As the proliferation of electric vehicles continues, safety issues related to secondary batteries have become prominent. In this study, a defect detection algorithm was developed by aligning visual images and active thermographic im-ages of the cylindrical secondary battery cap region. Lock-in amplitude images during the heating-cooling process using a laser were extracted to measure the difference in temperature patterns arising from internal structural variations. Ad-ditionally, to minimize the influence of surface conditions and temperature varia-tions other than laser-induced, a 10Hz periodic heating was employed, and ther-mal magnification in the heating frequency was achieved through data processing. This approach allowed the measurement of temperature changes induced by laser heating and cooling while minimizing the impact of surface conditions and tem-perature variations unrelated to the laser. A dataset of 1000 specimens was con-structed by aligning thermographic data with vision images acquired through this method. defective samples. Using this thermographic-vision alignment dataset, a defect detection algorithm was trained to achieve a more accurate and faster in-spection performance compared to existing non-destructive testing technologies.

Predicting 3D printed plastic part properties: A deep learning approach with thermographic and vibration data fusion

Additive Manufacturing (AM) holds transformative potential for the manufacturing industry, yet its widespread adoption is hindered by inconsistent product properties. This study addresses this challenge by pioneering a novel predictive method to assess the impact of in-process sensing on part property prediction in Fused Deposition Modeling (FDM), serving as a representative case study. Focused on five critical mechanical properties, including surface roughness, tensile strength, elongation at break, micro-hardness, and warpage, we systematically explore various sensor combinations by integrating Inertial Measurement Unit (IMU) sensors and a thermal camera with machine setting parameters. Utilizing deep learning models, specifically hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architectures, we encode time-series sensor data into signal images and evaluate performance across different sensor configurations. Our best-performing model achieves an impressive 99% correlation in predicting tensile strength, albeit with less robust performance in predicting warpage and micro-hardness, suggesting additional influencing factors. Furthermore, the employed signal imaging technique outperforms a handcrafted feature selection alternative. These findings carry significant implications for advancing the broader adoption of AM in critical applications, addressing challenges associated with inconsistent product properties and unlocking its full potential.

A Virtual Reality Environment Based on Infrared Thermography for the Detection of Multiple Faults in Kinematic Chains

Kinematic chains are crucial in numerous industrial settings, playing a key role in various processes. Over recent years, several methods have been developed to monitor and maintain these systems effectively. One notable method is the analysis of infrared thermal images, which serves as a non-invasive and effective approach for identifying various electromechanical issues. Additionally, Virtual Reality (VR) is a burgeoning technology that, despite its limited use in industrial contexts, offers a cost-effective and accessible solution for the training and education of industrial workers on specialized engineering subjects. Nevertheless, most virtual environments are based on numerical simulations. This paper presents the design and development of a Virtual Reality training module for the detection of fourteen electromechanical fault cases in a kinematic chain. The VR training tool developed is based on actual thermographic data derived from experiments conducted on an authentic kinematic chain. During these experiments, thermal images were captured using an low-cost infrared sensor. The thermographic images were processed by calculating the histogram and fifteen statistical indicators, which served to differentiate fault cases in the VR application. A comprehensive evaluation was carried out with a group of vocational students specialized in electrical and automation installations to determine the effectiveness and practicality of the VR training module.

The practical aspects of remote land sensing study of the causes of water penetration on ground hydraulic structures

This article is devoted to the application of remote sensing in the context of the natural and technical geosystem of the Dniester PSPP. The main emphasis is placed on the use of geographic information systems (GIS) and thermographic data to determine the factors that may cause abnormal thermal load on hydraulic structures. The study is aimed at identifying and analysing temperature gradients that may influence the occurrence of thermal anomalies. The study is aimed at identifying potential causes, mechanisms and factors affecting water manifestations. The subject of the analysis is also the relationship between the temperature gradient on the surface of structures and the presence of water leakage, as well as the impact of temperature on geological, hydrological and engineering systems of the structure. The analysis process is based on the interpretation of thermographic data reflecting the local heating of the surface of the hydraulic structure and the geotechnical characteristics of the soils, taking into account the design features of the hydraulic structure The research provides valuable insights that can be used to optimise the design and construction of earthen dams. Analysis of thermal processes is an important step in understanding and predicting their impact on the geological structure and hydrogeological properties of the environment. Man-made soils placed in the protective layers of a dam are subject to significant uneven heating. Keywords: geoecology, constructive geography, geosystems, river-basin systems, river-valley landscapes, river natural and technical systems, landscape technical systems, landscape engineering systems, GIS technologies, Dniester PSPP, remote sensing, thermo map, GNSS.

Objectively assessing visual analogue scale of knee osteoarthritis pain using thermal imaging

Knee osteoarthritis (KOA) is a common degenerative joint disorder that significantly deteriorates the quality of life for affected patients, primarily through the symptom of knee pain. In this study, we developed a machine learning methodology that integrates infrared thermographic technology with health data to objectively evaluate the Visual Analogue Scale (VAS) scores for knee pain in patients suffering from KOA. We preprocessed thermographic data from two healthcare centers by removing background noise and extracting Regions of Interest (ROI), which allowed us to capture image features. These were then merged with patient health data to build a comprehensive feature set. We employed various regression models to predict the VAS scores. The results indicate that the XGBoost model, using a 7:3 training-to-testing ratio, outperformed other models across several evaluation metrics. This study confirms the practicality and effectiveness of using thermographic imaging and machine learning for assessing knee pain, providing a new supportive tool for the management of pain in KOA and potentially increasing the objectivity of clinical assessments. The research is primarily focused on the middle-aged and elderly populations. In the future, we plan to extend the use of this technology to monitor risk factors in children’s knees, with the goal of improving their long-term quality of life and enhancing the overall well-being of the population.

Alternative data evaluation methodology for infrared thermography analogous to the Shock Response Spectrum analysis method

We propose an alternative method for processing infrared thermography (IRT) data based on a thermal 1-layer model. The method is based on the analogy of the Shock Response Spectrum (SRS) analysis for mechanical systems (ISO 18431). A thermal Q-factor, similar to the resonance sharpness (Q-factor) of a mechanical oscillator, is introduced as a parameter for selecting a specific thermal 1-layer model. Two main aspects of this method and corresponding properties have been considered: the strictly model-based treatment of this methodology and an empirical introduction of additional bandpass filters. The method is validated with two practical examples: for a homogeneous polymer sheet with blind holes and an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial defects, the capability of defect detection is compared with established methods. The comparison of the results with the commonly used data processing methods of IRT as Pulsed Phase Thermography (PPT) and Modified Differential Absolute Contrast (MDAC) shows equivalent or sometimes improved performance.