Thermographic Nondestructive Testing Research Articles

Application of residual network based on multispectral attention mechanism in stepped eddy current thermography defect detection

The stepped eddy current thermography nondestructive testing technique has a low and uneven temperature rise under weak excitation conditions, which brings great challenges to the quantitative analysis of defects in the inner wall of pressure vessels. To solve the above problems, this paper proposes a multi-spectral channel attention residual network model based on a Gramian angular difference field (GADF) for automatic detection and quantification. Firstly, the noise reduction and compression of temperature time series data are accomplished by singular value decomposition and piecewise aggregate approximation. Secondly, three infrared feature strategies commonly used for quantitative analysis were transformed by GADF and Gramian angular sum field (GASF) techniques to filter out the infrared feature data sets that contain the most defective feature information. Finally, the frequency domainization of the feature map is realized by embedding the multispectral channel attention in the residual network. The global dependencies and relevant essential features of infrared data are effectively captured, and the redundant feature information generated by deep learning feature maps is suppressed. The effectiveness of the model is verified by comparing several time-series models with advanced deep-learning models. The results show that the proposed model has excellent performance in terms of accuracy, stability, and generalization ability. Meanwhile, the model can effectively extract the defects in the region of uneven temperature rise.

Impact of Solar Shading on Façades’ Surface Temperatures under Summer and Winter Conditions by IR Thermography

In warm climates with high levels of solar irradiation, solar shading plays a determinant role on buildings’ envelope performance, both during summer and winter conditions. In this research, an evaluation of the solar shading effect on sunny façades through IR thermography non-destructive testing was performed. Sunny and shaded areas revealed temperature differences of 7.4 °C in summer conditions and up to 1.2 °C in wintertime. Moreover, solar shading was shown to be beneficial not only for decreasing surface temperature in summertime but also for reducing convective air flow in wintertime. In addition, it was found that the prevalence of dense shadows, especially with non-reflective materials in louvres, is favorable. External Thermal Insulation Constructive Systems (ETICS) must be shadowed and the use of clear colors is recommended to reinforce homogeneity in the surface in wintertime and reduce solar absorptance in summertime. Under steady-state calculations, thermal losses can be reduced up to 30% at night in wintertime and up to 50–60% at daytime in summertime because of the shadowing. However, another important finding lied in the confirmation of the performance gap that arises between using air temperature, sol-air temperature and the actual surface temperature data, in such a way that the two former implied high levels of inaccuracy and overestimated the performance of the buildings compared to the actual behavior. Some of the main conclusions can be extrapolated to other circumstances.

The apparent effusivity method for normalized thermal contrast evaluation in infrared thermographic testing

Thermographic nondestructive testing is used for the detection of near-surface discontinuities in materials, where indications of potential defects or discontinuities are detected and evaluated by their thermal contrast. The key requirements for successful thermographic data processing include managing non-uniform heating and identifying nominal areas. Advanced methods often rely on numerical simulation, which can be challenging and time-consuming. This study suggests a new simple method of thermal contrast calculation for flash-pulse thermography. It is based on the analysis of the inverse apparent effusivity. It includes an automated indication of the sound (defect-free) area and the creation of an artificial reference sequence, which is based on a temperature profile of the sound area and the non-uniform heating map. Derivative analysis of the smoothed contrast data is applied to improve detectability and Contrast/Noise Ratio (CNR). An experimental comparison of the proposed method with other known methods is presented. In the presented example, it allowed detection of 23 defects with an average CNR of 6.13 dB, against the traditional differential absolute contrast method, which detected only 13 defects with an average CNR of 0.53 dB. Thus, high detectability and high CNR values provided by the proposed method are demonstrated.

Parameters impact analysis of CFRP defect detection system based on line laser scanning thermography

ABSTRACT Carbon fibre reinforced polymer (CFRP) plays an increasingly important role in many fields, and the non-destructive testing (NDT) for its defects is drawing more and more attention. In this paper, a finite element model is established and the surface temperature difference between the defective and non-defective areas is selected as the characteristic quantity to systematically study the impact of three parameters, such as scanning direction, laser power and scanning speed, on the detection effect in the line laser scanning thermography NDT system. Qualitatively, the scanning direction is consistent with the carbon fibre orientation, the higher the laser power, and the slower the scanning speed is, the more favourable the defect detection. Further, the data obtained from the simulation are fitted, and the results show that the scanning speed and laser power have exponential and linear relationships with the maximum temperature difference, respectively. A related experimental system was established, the laser power was set to 7-, 10-, 13- and 15 W as well as the scanning speed to 10-,20-, 40- and 50 mm/s, to investigate the temperature variation of CFRP specimens containing artificial defects. The experimental results show that a greater laser power or lower scanning speed can obtain better defect detection compared with a smaller power or higher scanning speed.

Debonds and water-filled defects detection in honeycomb sandwich composites based on pulse infrared thermography ndt technique

Honeycomb Sandwich Composites (HSCs) have been extensively used in aerospace, automotive and shipbuilding industries due to their light weight, high temperature resistance, high strength and fatigue resistance. In this study, the infrared thermography was used to detect debonds and water-filled defects in HSCs specimens under pulsed thermal stimulation. To improve the efficiency of defects detection, dynamic thermal tomography (DTT), principal component analysis (PCA) and total harmonic distortion (THD) techniques were applied to the raw infrared image sequences. The results show that, in the inspection of HSCs, the defect identification results can be improved by using the image processing techniques mentioned above, while the signal-to-noise ratio (SNR) can be significantly improved by means of the THD technique. It is confirmed that debonds and water-filled defects in the HSCs can reliably be detected and identified by using the technique of pulse infrared thermography nondestructive testing.

Ermanno Grinzato’s contribution to infrared diagnostics and nondestructive testing: in memory of an outstanding researcher

ABSTRACT This paper is a tribute to Dr. Ermanno Grinzato, who passed away 10 years ago (1952–2012). We will review the original research and contributions that Dr. Grinzato made to infrared thermographic non-destructive testing and data analysis. Dr. Grinzato proposed and developed some novel techniques, including the evaluation of construction quality in civil engineering, the inspection of art objects, the detection of corrosion, etc. A number of his original concepts were further developed into new application areas, such as ultrasonic infrared thermography. Dr. Grinzato was on the organising committees of both the Thermosense (SPIE) and QIRT conferences, the leading conferences on the applications of infrared technology. He collaborated with other researchers around the world and was respected by all.

A method and apparatus for inspecting large flat composite parts by using the Infrared LST technique

The principle of Line Scan Thermography (LST) was used to develop a self-propelled infrared thermographic nondestructive testing device for the inspection of large, relatively flat composite aerospace parts, such as aircraft wings. The design of the unit allowed the suppression of noise from reflected radiation. The new equipment, using the LST method, provided defect detectability similar to that achieved with a classic, static, flash heating procedure, but with a higher rate of testing. Also, the line heating principle ensured more uniform thermal patterns, and the proper choice of scan speed and field of view allows the selection of optimal time delays and the creation of maps of defects at different depths. Defect characterization efficiency was improved by using a trained neural network.

Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries.

Increasing the efficiency of the quality control phase in industrial production lines through automation is a rapidly growing trend. In non-destructive testing, active thermography techniques are known for their suitability to allow rapid non-contact and full-field inspections. The robotic manipulation of the thermographic instrumentation enables the possibility of performing inspections of large components with complex geometries by collecting multiple thermographic images from optimal positions. The robotisation of the thermographic inspection is highly desirable to improve assessment speed and repeatability without compromising inspection accuracy. Although integrating a robotic setup for thermographic data capture is not challenging, the application of robotic thermography has not grown significantly to date due to the absence of a suitable approach for merging multiple thermographic images into a single presentation. Indeed, such an approach must guarantee accurate alignment and consistent pixel blending, which is crucial to facilitate defect detection and sizing. In this work, an innovative inspection platform was conceptualised and implemented, consisting of a pulsed thermography setup, a six-axis robotic manipulator and an algorithm for image alignment, correction and blending. The performance of the inspection platform is tested on a convex-shaped specimen with artificial defects, which highlights the potential of the new combined approach. This work bridges a technology gap, making thermographic inspections more deployable in industrial environments. The proposed fine image alignment approach can find applicability beyond thermographic non-destructive testing.

Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network

Quantitative defect and damage reconstruction play a critical role in industrial quality management. Accurate defect characterisation in Infrared Thermography (IRT), as one of the widely used Non-Destructive Testing (NDT) techniques, always demands adequate pre-knowledge which poses a challenge to automatic decision-making in maintenance. This paper presents an automatic and accurate defect profile reconstruction method, taking advantage of deep learning Neural Networks (NN). Initially, a fast Finite Element Modelling (FEM) simulation of IRT is introduced for defective specimen simulation. Mask Region-based Convolution NN (Mask-RCNN) is proposed to detect and segment the defect using a single thermal frame. A dataset with a single-type-shape defect is tested to validate the feasibility. Then, a dataset with three mixed shapes of defect is inspected to evaluate the method’s capability on the defect profile reconstruction, where an accuracy over 90% on Intersection over Union (IoU) is achieved. The results are compared with several state-of-the-art of post-processing methods in IRT to demonstrate the superiority at detailed defect corners and edges. This research lays solid evidence that AI deep learning algorithms can be utilised to provide accurate defect profile reconstruction in thermography NDT, which will contribute to the research community in material degradation analysis and structural health monitoring.

Super-Resolution Thermal Imaging Using Uncooled Infrared Sensors for Non-Destructive Testing of Adhesively Bonded Joints

To compensate for the inability of inexpensive uncooled infrared (IR) sensors to perform high-speed thermography detection with low noise and high sensitivity in the field of non-destructive testing (NDT), a post-processing method for uncooled IR sensors based on compressive sensing is proposed in this paper to perform super-resolution NDT thermography. Super-resolution IR phase images can be generated by first using a sparse representation of the low-resolution (LR) IR images and a sparse dictionary generated from randomly sampled high-resolution (HR) raw training image patches to generate an HR IR image sequence, and, subsequently, implementing IR thermography NDT. To verify the reliability of the proposed method, super-resolution IR images were combined with pulsed phase thermography (PPT) to verify bonded joint structures in carbon fibre-reinforced polymer (CFRP). Compared to other methods, the proposed method reduces the mean square error (MSE) by 11.13% and yields a better visual effect on the PPT phase image reconstructed using three-fold super-resolution. The proposed method not only produces sharp defect edges, but also retains the original texture, which is expected to enable wider application of uncooled IR sensors in NDT applications in the future.

Radiating power tracking based on fractional order sliding mode controller

The radiating power with sine waveform is required for modulated infrared thermography nondestructive testing. Sine voltage was applied by former authors for deriving sine power waveform. The theory mode of infrared lamp is proposed. According to numerical solution and FFT analysis, high frequency components are included in the radiating power waveform, when sine voltage is set to drive the lamp. A light intensity sensor was utilized for monitoring the real-time radiating power, and a close-loop power tracking system was constructed. Fractional order sliding mode controller was selected in the tracking system, for its robustness and flexibility property. The reachability of the controller is proved and the calculation formulas of reach time are given. In experiment, the tracking results with both PID and sliding mode controller were derived. Lower tracking overshoot and better robustness was derived through the sliding mode controller, compared with the PID counterpart.

Analyzing probability of detection as a function of defect size and depth in pulsed IR thermography

This study introduces a novel approach to the presentation of the probability of detection (POD) function in infrared (IR) thermographic nondestructive testing. The modified POD is suggested as a function of two defect parameters, namely, defect depth and lateral size. The proposed approach is based on calculating theoretical values of maximum temperature contrast for many defect size/depth combinations by using an appropriate analytical model. Furthermore, these values are used for the quantification of defects to produce predicted POD curves by applying a signal/response method. The results appear as the POD maps illustrating detectability of defects with various size/depth combinations. By setting a particular POD threshold, for example, 90%, the detectability limit contours can be obtained. These contours illustrate the limiting combinations of the depth and diameter of the defects, which can be detected with a required probability of correct detection under a particular temperature signal threshold. The proposed methodology is illustrated with an example of using the POD approach in pulsed IR thermographic inspection of a 3D printed specimen with artificial sphere-like defects. Such an approach allows predicting the detectability of defects in a vast range of depth/size ratios by using an analytical model and a limited number of experiments.

Tensor integrated total variation regularization for thermography NDT of composites

The carbon fiber reinforced polymer (CFRP) is being used frequently in the manufacturing of aerospace, rail, and other mechanical structures, where the optical pulse thermography (OPT)-based non-destructive testing (NDT) is generally used for the quality inspection, However, the output thermal sequences in OPT-based inspection suffer from uneven illumination and high-frequency thermal noise. Consequently, the inspection of defects (debonds) becomes difficult. To remedy it, post-image processing algorithms are generally carried out. The usefulness of such algorithms, however, is limited by the shape-complexity of the CFRP specimen. In this paper, we propose a tensor nuclear norm (TNN)-based low-rank and sparse total variation regularization (TVR) for CFRP debond defect detection. The integrated low-rank and sparse components are jointly and iteratively optimized. The proposed algorithm removes noise and segments/extracts the defects information from the thermal video sequences with improved resolution and contrast. Compared to the general image processing algorithm used for OPT-based NDT testing, the proposed algorithm is faster and in terms of F-score is more accurate.

Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

POSSIBILITY USING THERMOGRAPHIC CONTROL METHOD TO DETECT CRACKS IN REINFORCED CONCRETE STRUCTURES

Abstract. The article presents the study results of the possibility of using thermographic non-destructive testing to search for cracks in reinforced concrete structures. The basis of non-destructive thermal control is the registration of changes in the thermal field that occurs when the thermodynamic equilibrium of object with the environment is disturbed, which appears on the surface, and the nature of which allows obtaining the necessary information. The thermal control method is based on the interaction of the object's thermal field with thermodynamic sensitive elements (thermocouple, photodetector, liquid crystal element, bolometer), which convert the field parameters (intensity, temperature gradient, contrast, radiance) into an electrical signal of the recording device. As a result of the experiment, it was confirmed that the thermographic control method can be used when examining reinforced concrete structures for a qualitative assessment of hidden defects and damages. Thermal imagers with different resolution of the IR image matrix, an infrared thermo hygrometer, and a luxmeter were used as measuring instruments for the experiment. During the experiment, no fundamental difference in the use of thermal imagers with different types of IR matrices was revealed. When using the more accurate Flir E8 thermal imager, the thermogram had a clearer definition of the surface temperature compared to the Flir C2. This feature can be useful for quantifying defects, for example, the depth and size of a crack under a layer of paint, but fundamentally, for high-quality detection of defects and damage, the use of more accurate equipment is not a prerequisite. The authors conclude that the method of thermographic control can be used when examining reinforced concrete structures for a qualitative assessment of the presence of cracks.

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.

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.

Detection of Dangerous Defects and Damages of Steel Building Structures by Active Thermography

The article presents the results of a study of use thermographic non-destructive testing to search for cracks in steel structures. Theoretical substantiation of thermal non-destructive method of control for detection of cracks in steel structures. A practical study proving the possibility of using thermal non-destructive testing to detect cracks in steel structures is described. The problems that arise during the thermal non-destructive method of control and possible ways to solve them. The authors conclude that the method of thermographic control can be used during the inspection of steel structures for qualitative assessment of cracks.

A Review of Artificial Intelligence Applications in Thermographic Non-Destructive Testing

A Review of Artificial Intelligence Applications in Thermographic Non-Destructive Testing

ACTIVE THERMOGRAPHY FOR NONDESTRUCTIVE TESTING OF INSERTS IN MULTIMATERIAL SANDWICH STRUCTURES AND NUMERICAL SIMULATION

ACTIVE THERMOGRAPHY FOR NONDESTRUCTIVE TESTING OF INSERTS IN MULTIMATERIAL SANDWICH STRUCTURES AND NUMERICAL SIMULATION