Thermographic Signal Research Articles

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.

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.

Characterization of Multi-Layer Rolling Contact Fatigue Defects in Railway Rails Using Sweeping Eddy Current Pulse Thermal-Tomography

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency.

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.

Impact of the thermal afterglow effect on infrared thermography data evaluation methods

In this work, the influence of the afterglow effects from a source impulse on commonly used data processing methods of infrared thermography (IRT), such as Pulsed Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR) was investigated. To resolve this issue, we propose the adoption of a modified mathematical model or an empirical function to approximate the flash lamp pulse, which we recorded during the test with a photodiode. Two models were evaluated: one consisting of a combination of exponential and power functions describing the entire pulse and a second, focusing only on the decreasing phase. The models were compared with well-established empirical functions, such as those of Degiovanni and Vageswar, using the widely known statistical scores R2adjusted and Akaike information criterion. The quantitative comparison revealed better fitting results for our proposed models. Additionally, we introduce an algorithm based on Hough transform-clustering for automated determination of the exact pulse length. Statistical analyses were performed to evaluate the reproducibility of the measured pulse shape. The results from applying the first model as pulse correction is compared to another widespread IR data treatment methodology, such as Modified Differential Absolute Contrast (MDAC). We also consider the impact of the afterglow effects on the Source Distribution Image (SDI). Two different specimens were used in the experiments: a homogeneous polymer sheet with blind holes as defects and an in-homogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial defects made from Tetrafluorethylen-Hexafluorpropylen-Copolymer (FEP) foil inserts. In both experiments a quantitative comparison was made using the Tanimoto criterion (TC), indicating an improved sensitivity. This work forms the basis for a faster and more reliable quantitative evaluation of experimental data, in particular algorithms for the automated detection and assessment of manufacturing defects or in-service damage will benefit from these results.

Transient thermographic signal analysis for thinning detection in composite plate

Infrared thermography is an imaging technique that can be used to inspect materials for flaws and various degradations in a non destructive way. In this work, we focused on the use of fast models to recover information about the material properties from experimental measurements recorded over time. Two different modelling approaches are compared to each other and to experimental data acquired on a composite plate. Then, the model based inverse problem consisting in estimating the plate properties is discussed.

A reliability study on automated defect assessment in optical pulsed thermography

Nowadays, the reliability of data analysis and decision-making based on deep learning (DL) remains a primary concern in promoting DL technology for industrial non-destructive testing and evaluation (NDT&E). This study focuses on the quantitative assessment of the reliability of various automated data analysis techniques in NDT&E. To achieve this, optical pulsed thermography was employed to inspect three non-planar carbon-fiber-reinforced polymer (CFRP) samples, each containing embedded Teflon to simulate debonding defects. After applying thermographic signal reconstruction and first-order derivation processing to the raw thermal data, automated analysis was performed using three image-processing-based segmentation methods and three sequential-signal-based DL algorithms. Additionally, an experienced inspector manually analyzed the data for comparison purposes. Subsequently, the probability of detection and false positive analyses were conducted to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. The comparison results demonstrated that optimal DL classification and advanced image-processing-based segmentation techniques could achieve performance levels close to that of human inspectors in defect detection, even for challenging non-planar CFRP samples.

Improvement of 3D-Active Thermography by using Artificial Intelligence

For the one-dimensional case of heat propagation in active thermography the thickness of the investigated specimen can be directly reconstructed using known evaluation methods such as laser flash analysis or thermographic signal reconstruction and observation of a characteristic time. Concerning the multidimensional case diffusive effects have a strong impact on the heat propagation of the thermal wave, which leads to misinterpretations when evaluating the thickness especially at deep-lying edges and inhomogeneities in a specimen. The deeper a defect is located in the component, the more diffuse it is perceived in a change in surface temperature. Within this paper, heat flux simulations and real measurements of pulse thermography of different defect geometries are used to train a neural network for depth-resolved defect interpretation. The defect geometries are based on geometries of real impact damages in composites and were realistically obtained from a micromechanical fracture simulation. The neural network is based on an encoder-decoder approach where the temperature values of the cooling curve after a pulse-shaped excitation serve as input information. Segmentation is performed as a function of the backwall geometry. By training several thousand defect geometries using an encoder-decoder network, it was possible for the first time to directly infer the backwall geometry of a component without additional information about the component. Finally, it is shown how simulations can support the inversion of thermal waves for 3D-thermography of real measurements by an artificial intelligence system.

Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications.

This paper presents a novel method based on a convolutional neural network to recover thermal time constants from a temperature-time curve after thermal excitation. The thermal time constants are then used to detect the pathological states of the skin. The thermal system is modeled as a Foster Network consisting of R-C thermal elements. Each component is represented by a time constant and an amplitude that can be retrieved using the deep learning system. The presented method was verified on artificially generated training data and then tested on real, measured thermographic signals from a patient suffering from psoriasis. The results show proper estimation both in time constants and in temperature evaluation over time. The error of the recovered time constants is below 1% for noiseless input data, and it does not exceed 5% for noisy signals.

Intelligent Detection Algorithm Based on 2D/3D-UNet for Internal Defects of Carbon Fiber Composites

ABSTRACT Infrared thermography testing (IRT) has been widely used in the defect detection of composite materials. However, the identification of defects characteristics is unsatisfying due to the interference of factors such as uneven background and noise in the original thermal image sequence. A novel thermography-based defect detection method with the semantic segmentation network is proposed to enhance the defect contrast and extract perfect features. To Figure out the abnormal distribution of temperature field in thermal images, AG-UNet was used with a spatial self-attention gate module to extract spatial features of thermal images. The 3D-UNet network was obtained by the 3D convolution module and the temporal convolution module to extract thermal temporal and spatial features simultaneously which could help the defect segmentation in thermal videos. Compared with traditional algorithms such as principal component analysis (PCA), thermographic signal reconstruction (TSR), and fast Fourier transform (FFT), defects detection results were significantly enhanced with the proposed method, and defects of smaller diameter-to-depth ratio can be detected by deep learning models.

Quantitative characterization of archaeological bronzes based on thermal and compositional analysis

AbstractPulsed thermography has been applied to the quantitative characterization of the insertions of two ancient bronzes, the Boxer at Rest and the Hellenistic Prince. The analysis of the thermographic signal time dependence performed by a specifically developed model enabled the evaluation of the insertions’ thickness and of elements which could provide indications about the procedure followed for their insertion. This could be achieved by exploiting a semi‐empirical relation establishing the thermal diffusivity dependence on the total effective weighted concentration of Sn and Pb atoms obtained from the analysis of the values determined on samples containing different concentrations of Sn and Pb.

ADAPTION OF INDUSTRIAL NDT PROTOCOLS BASED ON ACTIVE INFRARED THERMOGRAPHY TO THE ART CONSERVATION WORLD: THE CASE OF THE WALL PAINTING AT HERODIUM

Abstract. Salt weathering is one of the most detrimental agents in the deterioration of historic wall paintings. Unfortunately, it is most noticeable once it reaches the surface, and the damage has already been done. Its elusive nature and its integration into the historic structures make it difficult to accurately detect soluble salts at the sub surfaces of wall painting non-destructively. Detailed detection in a delicate artifact such as wall paintings requires custom methodology. The study's case study is the wall painting in Herodium. The wall paintings were severely damaged by salt weathering. Although great efforts were invested in restoring and protecting the paintings their state is still unstable. This study offers a new perspective on the matter utilizing the adaption of industrial protocols based on active Infrared thermography (IRT) with data processing via thermographic signal reconstruction (TSR). The study's primary goal was to optimize the inspection methodology and assist the conservation process with knowledge of soluble salt hazards hidden at the subsurface.

Towards development of an intelligent failure analysis system based on infrared thermography

Electronic components of which reliability cannot be quantified are unacceptable and potentially hazardous, especially in safety-relevant areas such as driver assistance systems and medical technology where the zero-error principle applies. Reliability as a quality criterion has its origin in production, i.e. process variations have a negative influence on the structural integrity of the contact elements on the packaging and interconnect technology and, thus on the device performance in the field under thermo-mechanical load (temperature changes, vibration, humidity). At present, to ensure reproducibility of the reliability of each component, regular quality tests are often carried out in practice. However, a better and reliable approach will carry out 100 % inline checks for traceability and immediate readjustment. This work is the first step towards developing an intelligent non-destructive inline-capable failure analysis technique using infrared thermography. Good data forms the base on which robust and accurate AI algorithms can be trained and developed. However, the obtained thermographic images need to be processed so that subsurface defects can be detected. In this work, prominent algorithms, namely Pulse Phase Thermography (PPT), Thermographic Signal Reconstruction (TSR), Principal Component Thermography (PCT), Slope and Correlation Coefficient, have been thoroughly discussed and examined on the thermographic sequence from a plexiglass sample. A hybrid algorithm of TSR and PCT has also been suggested with promising results. In the end, potential post-processing algorithms from which the obtained results can be used for training an ML/AI model have been discussed.The major problem associated with the deep learning approach is the lack of data in the training set. The model's performance is dependent on the quality and quantity of the training data. The training data should be a good representation of the real-world scenario, i.e. it should be accurate enough and contain enough images, including exceptions for learning reliable features. However, getting sufficient data is a challenge in the manufacturing industry. However, using the Finite Element Model (FEM) approach for data generation can help overcome the data hurdle. In this paper, we also evaluate the potential application of FEM and the problems one faces when trying to generate a large amount of data for training using FEM.

IRT-GAN: A generative adversarial network with a multi-headed fusion strategy for automated defect detection in composites using infrared thermography

InfraRed Thermography (IRT) is a valuable diagnostic tool to non-destructively detect defects in fiber reinforced polymers. Often, a range of processing techniques are applied, e.g. principal component analysis, Fourier transformation, and thermographic signal reconstruction, in an attempt to enhance the defect detectability. Still, for the actual defect detection and evaluation, the interpretation by an expert operator is required which thus limits the (industrial) application potential of infrared thermography.This study proposes a Generative Adversarial Network (GAN) framework, termed IRT-GAN, to create a single unique thermal-image-to-segmentation translation of defects in composite materials. A large augmented numerical dataset has been simulated for a range of composite materials with different defects in order to train the IRT-GAN model. Integrated with the Spatial Group-wise Enhance layer, the IRT-GAN takes six pre-processed thermal images, thermographic signal reconstruction images in our case, as input and progressively fuses them via a multi-headed fusion strategy in the Generator. As such, this proposed IRT-GAN framework leads to the automated generation of a unique defect segmentation image.The high performance of the IRT-GAN, trained on the virtual dataset, is demonstrated on experimental data of both glass and carbon fiber reinforced polymers with various defect types, sizes, and depths. In addition, it is investigated how early, middle, and late-stage feature fusion in the GAN influences the segmentation performance.

Characterization of Wall-Loss Defects in Curved GFRP Composites Using Pulsed Thermography

Curved glass fiber–reinforced polymer (GFRP) composites are superior to alloy-steel pipes due to their excellent corrosive resistance properties, finding wide applications in the transportation of petrochemicals, chemical storage tanks, and power and water-treatment plants. Among the defects found in GFRP pipes, internal pitting or wall loss is one of the most severe, caused by material deterioration and the friction of small particles in the transfer fluid. This study investigates these in-service discontinuities using a pulsed thermal nondestructive evaluation technique. The paper focuses on the quantification of defect depth using the temperature peak contrast derivative and defect sizing using the full width at half maximum method. Further, the paper investigates the ability of pulsed thermography to estimate pitting or wall-loss defects at various depths and sizes through simulation and experimentation. Thermographic signal reconstruction images are used for quantification of defects at a deeper depth. The results of the present study are then compared with well-established ultrasonic C-scan results.

On the use of pulsed thermography signal reconstruction based on linear support vector regression for carbon fiber reinforced polymer inspection

ABSTRACT This study introduces and evaluates a new approach to reconstruct image sequences acquired during non-destructive testing by pulsed thermography. The proposed method consists of applying two linear support vector regressions to model the evolution of the data from both a spatial and temporal point of view. Each regression vectors will map the data with the number of pixels and the number of frames using convex optimisation. Then the regression vectors are used to predict a more robust representation of the data, thus reconstructing the sequence. The proposed method has been applied to data related to a reference sample of carbon reinforced fibre with known defects. This approach was evaluated on a sequence with severe non-uniform heating and was compared with state-of-the-art methods. Despite being sensitive to non-uniform heating, the proposed method provided a higher CNR score on smaller defects, compared with state-of-the-art methods. For the shallowest defects it shows better performance in term of contrast reconstruction compared to partial least-squares thermography (PLST). It also outperforms principal component thermography (PCT), and thermographic signal reconstruction-PCT (TSR-PCT) for defects located at a depth of 0.6 mm and 0.8 mm below the surface.

Three-Dimensional Printed Subsurface Defect Detection by Active Thermography Data-Processing Algorithm.

This article evaluates an active thermography algorithm to detect subsurface defects in materials made by additive manufacturing (AM). It is based on the techniques of thermographic signal reconstruction (TSR), thermal contrast, and the physical principles of heat transfer. The subsurface defects have different infill, depth, and size. The results obtained from this algorithm are compared with state-of-the-art TSR technique and show the high performance of the proposed algorithm even for subsurface defects done by 3D AM. The resulting images are better shown using the absolute difference in the place of variance. The proposed algorithm has higher contrast, better sensitivity to the defect depths, and lower noise than the TSR. The resultant image is quite clean and gives no doubt where the subsurface defects are.

Problems of Active Dynamic Thermography Measurement Standardization in Medicine

Reliability of thermographic diagnostics in medicine is an important practical problem. In the field of static thermography, a great deal of effort has been made to define the conditions for thermographic measurements, which is now the golden standard for such research. In recent years, there are more and more reports on dynamic tests with external stimulation, such as Active Dynamic Thermography, Thermographic Signal Reconstruction or Thermal Tomography. The subject of this report is a discussion of the problems of standardization of dynamic tests, the choice of the method of thermal stimulation and the conditions determining the credibility of such tests in medical diagnostics. Typical methods of thermal stimulation are discussed, problems concerning accuracy and control of resulting distributions of temperature are commented. The best practices to get reliable conditions of measurements are summarized.

Exploratory factor analysis for defect identification with active thermography

Non-destructive testing (NDT) methods are commonly used to disclose defective inner structures of materials, where active infrared thermography is a popular NDT technique because of its easy operation, low cost, and the capability of rapid scanning of large areas. However, the thermographic signals often suffer from noise and non-uniform backgrounds, making defect identification difficult. Therefore, an additional processing step of thermographic data is often required to enhance the contrast between defects and their surroundings. In recent years, multivariate statistical analysis methods have been widely adopted to achieve this aim, among which principal component thermography (PCT) is a typical example. Sparse PCT (SPCT) further improves PCT by adding sparsity constraints to the optimization problem. Nevertheless, the performance of SPCT depends on the subjective selection of the tuning parameters. The optimal parameter values are case-dependent and unknown. In this work, an alternative thermographic data analysis method is proposed based on exploratory factor analysis (EFA). By means of factor rotation, EFA minimizes the complexity of factor loadings and makes the results more interpretable. In doing this, EFA provides results similar to those of SPCT, while there is no need for parameter selection. Experimental results illustrate the feasibility of the proposed method.

A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure

Electronic supply chain vulnerability has been threatened by counterfeits for decades and as a result, the authentication and quality of Electronic Components (ECs) are highly valued by stakeholders for safety-critical industrial systems. Thermography Nondestructive Testing (NDT), offering a rapid inspection while covering a large area within a short time frame, is a promising technique to inspect the integrity of ECs. However, recognising the internal structural layout directly through a packaged EC from thermography images is still a challenge due to its sealed and cramped structure. In this research, we propose a Pulsed Thermography (PT) based data analysis method for enhancement and identification of the layout of die and lead frame in ECs. The proposed solution combines the Sliding Window (SW) concept, Thermographic Signal Reconstruction (TSR) and Principal Component Thermographic (PCT) technique, named SWPCT, to enhance the contrast of die, die substrate and lead frame. Compared to the conventional full-scale PT image analysis, the proposed method presents its superior sensitivity in small thermal features by reducing the spatial thermal information redundancy benefiting from the sliding window PCT analysis. It overcomes the structural thermograms obstruction from the thick packaging, reveals the hidden die and lead frame layout and suppresses the noise simultaneously. By selecting a proper window size, this paper reveals that SWPCT can reconstruct the position and dimension of die and die substrate in ECs, and spatially distinguish the lead frame layout. Two groups of typical OpAmps chip samples with internal structure diversity (X-ray observed) were tested using PT to demonstrate the effectiveness of the proposed method. Simulation data were also used to verify this approach. This research could unlock the challenge of thermography-based inspection for key internal structure in the encapsulated integrated components.