Active Infrared Thermography Research Articles

Enhancing defect detection in active infrared thermography using adaptive background suppression techniques

Enhancing defect detection in active infrared thermography using adaptive background suppression techniques

Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm

ABSTRACT As the demand for renewable energy sources increases, the vulnerability of solar panels to lightning strikes becomes a critical concern. This research explores the correlation between lightning-induced voltage fluctuations and the resultant damage intensity on solar panels. The study adopts a systematic approach, first investigating the correlation between lightning-induced voltage assessment using 30kV, 60kV and 90 kV impulse voltage with multi-stage Marx impulse generator and the damage intensity on monocrystalline and polycrystalline solar panels. A portable active infrared thermography equipment with tCam-Mini was employed to conduct this research with wireless streaming. Furthermore, the study integrates Convolutional Neural Network (CNN)-based image classification techniques to enhance the efficiency of damage assessment. The results highlight the potential of neural networks in improving the accuracy and speed of image classification for damaged and undamaged samples. The findings contribute valuable insights into enhancing the resilience of solar panel systems against lightning strikes, ultimately advancing the reliability and sustainability of solar energy infrastructure. A new CNN model was developed to classify the images obtained from thermography with 90.21% accuracy for greyscale and 85.31% accuracy on thermal images.

Code-specified early delamination detection and quantification in a RC bridge deck: passive vs. active infrared thermography

Code-specified early delamination detection and quantification in a RC bridge deck: passive vs. active infrared thermography

DBHF: A double fusion backbone and bidirectional feature hybrid fusion detector for high-precision inspection of avionics solder joint defects

The quality of avionics thermistor wire solder joints has a critical impact on the performance of in-orbit spacecraft. However, it is still hard to simultaneously realize a high-precision inspection of the multi-scale internal and external defects of avionics solder joints. Herein, in combination with active infrared thermography technique, an innovative DBHF detector with a double fusion backbone and a learnable weighted multi-scale bidirectional feature hybrid fusion pyramid network (BdFPN) is demonstrated based on RT-DETR framework. Specifically, the double fusion backbone framework comprises with two lightweight convolutional neural network-based backbones. Compared with conventional single backbone, the double-backbone design forms a multi-level Resnet-like structure, helping to enrich the extracted multi-dimension feature information and retain rich detail information of small-scale defects. The complex learnable weighted multi-scale BdFPN is designed for cross fusion of deep features. One or more additional multi-scale inputs are added to each node involved in feature fusion to aggregate more features from multiple scales and strengthen the interaction between low-level details and high-level semantic information, enabling the detector to make full use of multi-level feature information. Meanwhile, a learnable weight coefficient is set on each node involved in feature fusion and the importance of the input to each node is estimated by training, so that the effective information can be deeply aggregated at different levels. Our DBHF detector achieves a 92.7 % mAP@0.5 on a self-made avionics solder joint dataset with a body size of only 27.2 MB and FPS of 149, outperforming all the typical advanced real-time detectors.

One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

Fiber-reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive testing (NDT) methods to ensure structural integrity and safety. Active infrared thermography (AIRT) is a fast and cost-efficient NDT technique for inspecting FRP composites. However, this method is easily affected by factors such as inhomogeneous heating, leading to a low level of visualization of defects. To address this issue, this study proposes a novel method called one-dimensional deep convolutional autoencoder active infrared thermography (1D-DCAE-AIRT) to enhance the visualization of internal defects in FRP composites. This method first preprocesses the thermal image sequence acquired by AIRT inspections. Subsequently, high-level thermal features at the pixel level are extracted from the aforementioned preprocessed thermal image sequence using a designed one-dimensional deep convolutional autoencoder (1D-DCAE) model. Finally, the extracted high-level thermal features are employed to generate enhanced visualization results that exhibit improved defect visibility. The results of three kinds of AIRT (eddy current pulsed thermography, flash thermography, and vibrothermography) experiments on FRP composite specimens with artificially introduced defects show that 1D-DCAE-AIRT can effectively enhance the visualization of internal defects. The enhancement effect is better than the conventional techniques of fast Fourier transform (FFT), principal component analysis (PCA), independent component analysis (ICA), and partial least-squares regression (PLSR).

Non-Invasive Detection of Hidden Corrosion in Aluminum Sheets

Non-invasive detection of hidden defects using infrared thermography (IRT) mainly focuses on defects involving material loss. Of equal importance is the detection of hidden defects resulting from corrosion. This study proposes an improved method of detecting hidden defects caused by corrosion through active IRT. Aluminum sheets with or without hidden corrosion were identified using two simple analysis approaches, (1) observing differences in their mean surface temperature and visual thermal contrast and (2) comparing the temperature probability distribution of identified surfaces at a given reference temperature. The developed method only requires heating the region of interest to <66 °C with a portable heat source and monitoring the surface temperature distribution using IRT. This method may aid in building surveys and post-monitoring of metallic building envelopes, especially indoors or when environmental conditions cannot provide the needed thermal contrast for detecting hidden corrosion.

Nondestructive evaluation of cooling passages and their blockage using vapor excited active thermography

ABSTRACT Non-destructive testing of cooling passages has attracted a considerable interest due to the critical rule of these segments in the performance and endurance of different parts. In this research, the performance of active infrared thermography in the non-destructive inspection of cooling passages located inside the metallic bases has been investigated. Water vapour was selected as an excitation fluid so as to create the thermal contrast. Specimen material, hole depth, hole diameter and injection pressure were selected as the input parameters effecting on the thermal contrast. The results obtained via statistical analysis revealed that the hole depth and injection pressure have the noticeable influence on the dimensionless output temperature recorded with thermal camera. A 50% decrease in dimensionless temperature contrast has been revealed when the hole depth has become four times. Besides, a similar increase in injection pressure, elevated the above-mentioned parameter almost 20%. In contrast, the parameters of specimen material and hole diameter specified no perceptible effects. Moreover, infrared thermography with water-vapour excitation proved proper capability in detection of 60% and 80% blockage located inside the holes.

Image segmentation for automatic detection and detectability evaluation of thinning defects using LSM-based induction thermography

The infrared thermography (IRT) technique is a highly reliable and attractive technique that can evaluate a large area in real-time without destroying the inspection object. This study used the line scanning method (LSM)-based induction thermography (IT) technique among active IRT to detect thinning defects in S275 steel specimens. Unlike the dynamic mode, LSM was applied to perform sequence processing so that the copper coil shape was removed in the 2D thermal images processed. After image segmentation, a binary image was acquired using the Otsu algorithm. By utilizing image segmentation, the applied threshold for each defect is different when performing the Otsu algorithm. This makes it possible to compare defect detection according to the presence or absence of image Segmentation. Automatic defect detection was performed using a Boundary Tracking algorithm, and 12 defect detections were confirmed. Accuracy was evaluated through pixel calculation, and defects in A, B, and D columns showed high detection accuracy. This study presented the principle of LSM in which includes the relative movement between the IR camera and the specimen. In addition, a mechanism to increase the number of defects detected through image segmentation was presented.

Self-stimulating sandwich structure with resistive heating honeycomb core for quick thermographic inspection

The paper presents the novel concept of a honeycomb sandwich structure with carbon fibre reinforced plastics face sheets capable of its own internal thermal stimulation for rapid active thermographic inspection, where an adapted electrically powered honeycomb core serves as a heat source. The proposed sandwich structure effectively reduces the equipment necessary for external thermal stimulation, while improving the controllability of the thermal pulse. A new design of the modified aluminum honeycomb core, required for resistance heating with sufficient homogeneity, is proposed. Numerical modeling was used to test the concept’s viability and to predict its efficiency for defect detection. The altered honeycomb was then manufactured and its heating characteristics were measured. This structure was then used as a core in a sandwich specimen with carbon fibre reinforced plastics face sheets, which also contained artificial defects embedded in the face sheets and in the face sheet/core interface. The applicability of the proposed modified honeycomb structure for thermal stimulation for active infrared thermography was experimentally confirmed, demonstrating the ability to visualize even very small defects simulating disbonding, or delamination. The impact of water ingress on the operation of the concept was tested as well. The proposed method has the potential to simplify and expedite the non-destructive inspection of aviation-grade sandwich structures in service via active infrared thermography.

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.

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Active infrared thermography (IRT) has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis (PCA) algorithm with sliding-window mechanism and the one dimensional—residual neural network—convolutional block attention module (1DResnet50_CBAM) model was proposed for the reconstructed image sequence of active IRT to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using PCA algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional defect data were transformed into 1D temporal infrared thermal signal. Moreover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion PCA algorithm with sliding-window mechanism and 1DResnet50_CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and quantitative analysis using infrared thermal detection technology.

Diffusion-compensated correlation analysis of frequency-modulated thermal signal for quantitative infrared thermography

Application of a broadband modulated excitation signal and its cross-correlation with the corresponding thermal response, so-called thermal wave radar or pulse compression thermography, is a well-known technique in active infrared thermography. The technique benefits from both the broadband spectrum and the pulse compression efficiency of excitation signal, providing lag and phase quantities which can be used for characterization of defects. However, the significant distortion of the thermal response due to heat diffusion makes the cross-correlation analysis inefficient, particularly for the detection of deep defects or the inspection of a material with low thermal diffusivity. To tackle this issue, diffusion-compensated correlation analysis (DCCA) of thermal signal is proposed for quantitative infrared thermography, using a frequency-modulated sweep signal as excitation waveform. DCCA is based on the correlation of the measured thermal response with a library of template thermal responses calculated using the 1D analytical solution of the heat diffusion problem. DCCA can then more reliably resolve the thermal response in the presence of measurement noise, and depending on the definition of the library, directly map it to the corresponding depth or diffusivity. Two inspection modes are studied: (i) transmission of heat from defects which act as sub-surface heat sources (e.g. vibrothermography), and (ii) reflection of an externally applied heat flux from sub-surface defects (e.g. optical infrared thermography in reflection mode). Outperformance of DCCA to thermal wave radar is analytically substantiated and verified by finite element simulation on a carbon fibre reinforced polymer plate. Furthermore, the technique is experimentally studied for thermographic inspection of a carbon fiber reinforced polymer (CFRP) coupon including artificial defects, and the advantages as well as limitations of the technique are discussed.

Qualitative Comparison of Lock-in Thermography (LIT) and Pulse Phase Thermography (PPT) in Mid-Wave and Long-Wave Infrared for the Inspection of Paintings

When studying paintings with active infrared thermography (IRT), minimizing the temperature fluctuations and thermal shock during a measurement becomes important. Under these conditions, it might be beneficial to use lock-in thermography instead of the conventionally used pulse thermography (PT). This study compared the observations made with lock-in thermography (LIT) and pulse phase thermography (PPT) with halogen light excitation. Three distinctly different paintings were examined. The LIT measurements caused smaller temperature fluctuations and, overall, the phase images appeared to have a higher contrast and less noise. However, in the PPT phase images, the upper paint layer was less visible, an aspect which is of particular interest when trying to observe subsurface defects or the structure of the support. The influence of the spectral range of the cameras on the results was also investigated. All measurements were taken with a mid-wave infrared (MWIR) and long wave infrared (LWIR) camera. The results show that there is a significant number of direct reflection artifacts, caused by the use of the halogen light sources when using the MWIR camera. Adding a long-pass filter to the MWIR camera eliminated most of these artifacts. All results are presented in a side-by-side comparison.

Nondestructive detection of delamination in painted concrete structures through square pulse thermography

Active infrared thermography (IRT) is effective for detecting and characterizing subsurface defects in concrete structures. Even though painted concrete (PCo) structures have been applied worldwide (especially for buildings) and subsurface defects, such as delaminations, can develop in their core, active IRT-based defect detection in previous studies focused on normal concrete (NCo) structures rather than PCo structures. Hence, the detectability of delaminations in NCo and PCo structures is investigated in the present study by applying square pulse thermography (SPT), a common approach for active IRT. The results demonstrated that delaminations in PCo, under the same parameters, are more detectable in IR images than in NCo structures. In addition, the effect of the width-to-depth ratio of a defect in PCo structures on the detectability during SPT inspections is presented. Interestingly, overheating is evaluated to determine an effective heating regime for SPT surveys on PCo structures. Finally, the empirical thermal diffusivity coefficient of PCo structures is estimated, which has not been previously determined.

A global-local damage localization and quantification approach in composite structures using ultrasonic guided waves and active infrared thermography

The paper emphasizes an effective quantification of hidden damage in composite structures using ultrasonic guided wave (GW) propagation-based structural health monitoring (SHM) and an artificial neural network (ANN) based active infrared thermography (IRT) analysis. In recent years, there has been increased interest in using a global-local approach for damage localization purposes. The global approach is mainly used in identifying the damage, while the local approach is quantifying. This paper presents a proof-of-study to use such a global-local approach in damage localization and quantification. The main novelties in this paper are the implementation of an improved SHM GW algorithm to localize the damages, a new pixel-based confusion matrix to quantify the size of the damage threshold, and a newly developed IRT-ANN algorithm to validate the damage quantification. From the SHM methodology, it is realized that only three sensors are sufficient to localize the damage, and an ANN- IRT imaging algorithm with only five hidden neurons in quantifying the damage. The robust SHM methods effectively identified, localized, and quantified the different damage dimensions against the non-destructive testing-IRT method in different composite structures.

In-depth defect inspection of precast double walls using non-destructive techniques

Precast double walls are frequently used for building applications and consist of a pair of precast reinforced concrete shells connected by a lattice girder creating an inner cavity. Once placed at its final position this cavity is filled with freshly cast and compacted concrete. Aside from benefits such as increased ease in installation, reduced cost and shorter construction time also some pitfalls are recognized. Due to the restrained space and the presence of the truss girder undesired defects (e.g. voids, honeycombing, etc.) might occur without being noticed, as the outer wall elements also act as formwork. This actually might reduce the load capacity and jeopardize the structural integrity of the concrete structure. Therefore, different non-destructive tests are being used in order to assess the filling degree of the cavity in the double walls and to localize defects, with varying success ratio (i.e. the detectability). Different techniques were applied on a test wall with intentionally embedded defects (polystyrene blocks) with varying diameter: rebound hammer, ultrasonic pulse velocity via direct transmission, ultrasonic shear-wave tomography, ground penetrating radar and active infrared thermography. The success and the limits of these techniques for in depth defect localization are being discussed in this paper. The effect of the selected detection method and the surface-on-depth ratio of the defect itself has an effect on its detectability.

Nondestructive Evaluation of Composite Bonding Structure Used in Electrical Insulation Based on Active Infrared Thermography.

Nondestructive testing and evaluation of composite insulating components of electrical equipment is extremely necessary for assuring the safety of a power system. However, most existing nondestructive testing methods are not applicable for fast and effective live detection due to their time-consuming operation, high cost, and contact or near-field measurement. In this work, the effectiveness of active infrared thermography was investigated for detecting defects in silicone rubber (SIR)–fiber-reinforced plastic (FRP) bonding structures, which have been commonly used in insulating components of power equipment. The effectiveness of differential thermal image for enhancing the contrast of defective and sound areas and eliminating additive noise was demonstrated. Particularly, frame difference thermal image obtained by subtracting two differential thermal images extracting from respectively before and after the contrast inversion was proposed to enhance defect identification. The results revealed that defects of various sizes and depths such as voids, cracks, and interface disbonding of the SIR–FRP bonding structure were accurately detected by thermographic data. With the advantages of a quick and simple process, safety, universal applicability, visual results, far-field measurement, and quantitative defect estimation capabilities, active infrared thermography would be quite promising for live detection of electrical equipment.

Semantic segmentation for non-destructive testing with step-heating thermography for composite laminates

In this paper, semantic segmentation networks such as UNet and DeepLabV3+ are evaluated and compared against Random Forest and Support Vector Machines in the field of step-heating active infrared thermography for subsurface defect detection and localization. To collect information from an entire digital recording sequence into a particular image, post-processing methods such as PCT, PPT, Kurtosis, Skewness and TSR are used. Two datasets are created, one with 3-channel images using PCT, and one using all the above post-processing methods to condense the heating and cooling processes into 30-channel images. This evaluation study shows that DeepLabV3+ is able to detect most defects in specimens with a similar structure to training samples without false positives even for defects of different depth and area. UNet requires the use of 30-channel images to achieve results closer to DeepLabV3+. Random Forest and Support Vector Machines are unable to compete with the recent methods as they are unable to detect defects correctly.

Qualitative evaluation of 3D printed materials’ structure using active infrared thermography and signal analysis based on LSTM neural networks

In this article we present an approach to the quantitative evaluation of the 3D printed sample made of polyethylene terephthalate glycol (PETG) using the active infrared thermography (AIT) method with halogen lamps excitation. For this purpose, numerical and experimental studies were carried out. The numerical model solved with finite element method (FEM) was used first to create a database of signals and further to train neural networks. The networks were trained to detect the heterogeneity of the internal structure of the tested printed sample and to estimate the defects position. After training, the performance of the network was validated with the data obtained in the experiment carried out with the active thermography regime on a real 3D print identical to the modelled one.

Noninvasive Methods of Active Thermographic Investigation: Short Overview of Theoretical Foundations with an Example of Application

The field of non-destructive testing using thermography (NDT—Non-Destructive Testing and NDE—Non Destructive Evaluation) was established in the 1960s as a competitive and complementary method to ultrasonic or X-ray testing. Non-destructive quality control of objects is essential in modern industry, especially in mass production. It is an integral part of the quality control process. The reason for the introduction of non-destructive testing into diagnostic techniques were disasters and failures of various equipment and structures. Over the years, a division has been drawn between passive and active thermography. The aim of this publication is to present the theoretical basis of non-destructive testing with the use of active infrared thermography methods. It describes selected models of transient heat flow (thermal wave) in objects of various shapes and physical properties. They are the basis for understanding the changes in the surface temperature of the studied objects in time, which are the response to thermal excitation of different nature. An example of their effective application in engineering practice is the author’s software, in which the recorded time response is matched to the analytical solution, and on this basis detected anomalies are parameterized, detected by the fusion of various techniques of thermal image processing and analysis methods.