Thermographic Inspection Research Articles

Maintenance and inspection applied to photovoltaic solar plant, a case study at UFERSA-Caraúbas

The growing expansion of photovoltaic systems results in the need to perform maintenance routines and inspections in order to ensure that components and equipment function as expected, that is, normal productivity, operate with low risks to health, environment and safety. In this work, an analysis was carried out in the photovoltaic solar plant of UFERSA-Caraúbas, which included visual inspection and qualitative thermographic analysis, where the first one had the purpose of verifying the components and the photovoltaic arrangement itself, evidencing the factors considered abnormal; and the second, to recognize possible thermal anomalies present in the PV system and/or its components. Finally, it was possible to verify that the system in question has a considerable amount of modules with broken surface glass, in addition to the relevant presence of dirt on their surfaces. The thermographic inspection showed that the modules together with the inverters remain within the limit range determined by the manufacturers.

Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction

In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.

Fatigue health monitoring of AISI 304 notched specimens by means of thermographic analysis and UT-based measurements

The damage accumulation and the cracks development due to fatigue loads are the main failure mechanisms that occur in metal structures. Therefore, the prediction of structural damage is critically important for the safe and reliable operation of complex engineered systems. In the present work, the evolution of fatigue damage was monitored by infrared thermographic and ultrasonic measurements by means of piezoelectric sensors (PZTs) applied to a batch of notched specimens in AISI 304 stainless steel. Concerning the ultrasonic data, a linear and non-linear frequency study was carried out on the data acquired at various steps during the fatigue life through an appropriate MATLAB analysis algorithm based on the Fast Fourier Transform (FFT). In details, the proposed damage parameters related to thermal variations and the peak-to-peak signal voltage (Vpp) were considered to evaluate the fatigue damage process. Regarding the IRT technique, the thermographic monitoring inspection is implemented with measurements processed in MATLAB to estimate damage indices related to the fatigue damage of the same specimens. The results of the thermal analyses were finally compared with the data of the ultrasound measurements obtained to provide complementary information on the prediction of fatigue damage. The experimental results indicate an interesting increase in the proposed damage parameters that could be associated with an irreversible change due to fatigue damage at the notch tip. The final propagation phase of the crack, instead, is characterized by a quick data variation in agreement with the stiffness reduction. The applied Monitoring strategies has proved efficient for detecting damage induced by fatigue in metal materials.

The use of induction heating for thermographic inspection of composite laminates

In the present paper, an active thermographic technique is proposed for the inspection of a unidirectional GFRP panel with integrated artificial internal defects and an aeronautical CFRP panel with real small production defects.Different inductor geometries were considered by varying the length and the number of spires, finally allowing to identify the optimal solution. Several tests allowed to identify the optimal experimental setup for defect detection as a function of the location of the defect.The acquired thermal maps were processed in Matlab with a suitable analysis algorithm for the determination of the absolute and normalized thermal contrasts. Another important consideration concerns the possibility of obtaining a qualitative indication on the defect depth. Although the thermal contrast depends on the choice of the intact and defective area, the maximum value of the absolute contrast was higher for the more superficial defects than for the deeper ones. Furthermore, the absolute contrast was correlated with the size of the defects. In the study of CFRP panel, the defects characterization was conducted in a comparable way to evaluate the contrasts with a suitable choice of undamaged reference area.The comparison of the proposed technique with the traditional pulsed technique has demonstrated the real potential of application on SMArt GFRP and aeronautical CFRP elements with the same level of reliability as traditional thermographic techniques. However, the induction technique has shown better ability to detect small real defects more accurately in CFRP panels. However, the limitation of the power generator and the size of the inductor could limit the detectability of defects.

Thermographic Inspection Simulation of One Kind of Rail Defects

Rail is an important foundation of rail transit, and defects on the rail may pose a serious threat to rail transit. As a new non-destructive testing method, thermographic detection shows an excellent application prospect. In order to verify whether thermographic detection technology can be used to detect the defects on the rail, especially the defects at the bottom of the rail. The ANSYS software is used to model the rails under different conditions, and the heat flow simulation experiment is carried out to obtain the temperature change results when the heat flow passes through the defects and when it does not. The existence and location of defects can be determined by observing how the heat flow changes due to the existence of defects.Although the results show that surface fracture defects and defects towards the bottom edge of the rail can be clearly detected by thermal modeling under laser heating conditions, for transverse cracks that exist near the center of the rail bottom, the simulation experiments still cannot distinguish the temperature differences at the same observation location under current parameters. So it may be necessary to further optimize the experimental process, or increase the experimental parameters, so that the thermal modeling method can be better applied for non-destructive testing of rail defects.

Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection

With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.

Redundant Fault Diagnosis for Photovoltaic Systems Based on an IRT Low-Cost Sensor.

In large solar farms, supervision is an exhaustive task, often carried out manually by field technicians. Over time, automated or semi-automated fault detection and prevention methods in large photovoltaic plants are becoming increasingly common. The same does not apply when talking about small or medium-sized installations, where the cost of supervision at such level would mean total economic infeasibility. Although there are prevention protocols by suppliers, periodic inspections of the facilities by technicians do not ensure that faults such as the appearance of hot-spots are detected in time. That is why, nowadays, the only way of continuous supervision of a small or medium installation is often carried out by unqualified people and in a purely visual way. In this work, the development of a low-cost system prototype is proposed for the supervision of a medium or small photovoltaic installation based on the acquisition and treatment of thermographic images, with the aim of investigating the feasibility of an actual implementation. The work focuses on the system's ability to detect hot-spots in supervised panels and successfully report detected faults. To achieve this goal, a low-cost thermal imaging camera is used for development, applying common image processing techniques, operating with OpenCV and MATLAB R2021b libraries. In this way, it is possible to demonstrate that it is achievable to successfully detect the hottest points of a photovoltaic (PV) installation with a much cheaper camera than the cameras used in today's thermographic inspections, opening up the possibilities of creating a fully developed low-cost thermographic surveillance system.

An efficient parametrized optical infrared thermography 3D finite element framework for computer vision applications

Matching infrared thermography (IRT) with deep learning-based computer vision has recently gained a lot of interest for automated defect assessment in materials. One of the remaining bottlenecks concerns the necessity of a large and diverse experimental and/or virtual training dataset in order to achieve a sufficiently generalizable computer vision algorithm. This paper presents a parametrized 3D finite element (FE) framework, implemented in Fortran90, for efficiently simulating optical infrared thermographic inspection of multi-layer anisotropic media and establishing large-scale virtual dataset with sufficient diversity. The interface element is introduced for the modelling of an imperfect thermal contact, allowing to simulate a variety of defect types. The flexibility of the interface element makes it possible to simulate delaminations with different thickness using the same discretized model. Validation is done for two benchmark cases which are representative for a fiber reinforced polymer laminate with delamination-like defects. In order to achieve true-to-nature thermographic simulation data, non-uniform heating conditions are adopted from experiment, and a stochastic morphology generator is introduced for modelling realistic irregular defect geometries. To demonstrate the added value of a large, diverse and true-to-nature virtual database for computer vision applications, a Faster-RCNN model was trained on a generated virtual dataset for the detection of delamination-like defects in fiber reinforced polymer laminates. Application of the trained Faster-RCNN on experimental thermographic data yields excellent inference results, illustrating the high generalization ability of the virtually trained object detector.

Enhanced thermographic inspection of woven fabric composites by [formula omitted]-space filtering

Infrared thermography is a well-known non-destructive testing technique for detecting defects in unidirectional fiber reinforced polymer laminates. When applied to woven fabric reinforced polymers, however, the weave structure causes strong disturbances and patterns in the background of the thermal images, making accurate and reliable defect assessment a challenging task. In this paper, the concept of k-space filtering is introduced for an improved evaluation of thermographic images obtained from woven fabric composites. An algorithm is introduced to automatically decompose a thermographic image into an image which contains the structured thermal background related to the weave pattern, and a residual image representing other features, e.g. defects. The proposed k-space filtering approach is demonstrated on thermographic data from various woven fabric composites with different weave patterns and defect scenarios, clearly showing an enhanced performance in terms of defect detection and sizing.

Suppressing reflected radiation to enhance infrared thermography inspection

Infrared thermography is commonly used in a variety of applications. It is a fast, passive, non-contact, non-invasive alternative to conventional techniques. Nevertheless, its use has several associated errors that must be minimized or eliminated. One source of error is the radiation reflected on the surface of the studied object, coming from the surroundings, and captured by the thermographic camera lens. A measurement of the reflected apparent temperature may compensate for this error. In fact, during the thermographic inspections, the operator generally assumes that the reflected apparent temperature is equal to the ambient temperature, resulting in a less accurate thermal image pattern. This study aims to verify the impact of suppressing reflected radiation on improving the thermal image details of the thermograms obtained during thermographic inspections. For this purpose, a wood sample was observed in the presence and absence of heat and light sources. The results suggest that the suppression of reflected radiation leads to better-quality thermograms and enhances the accuracy of defect detection and identification.

Considerations Regarding Electrical Equipment Monitoring Through Infrared Thermography

Abstract In the paper are highlighted the main advantages of wireless monitoring methods present in the market for electrical equipment temperature surveillance. Also, are considerate the mandatory corrections applied to the thermographic image in order to adjust the electrical equipment temperature. In the final part, there are shown the resulted values of an infrared thermography inspection performed in industrial environment, which reveals the thermal stresses identification of the busbar’s connections from a medium voltage switch separator. In addition, the paper present why a highly reflexive surface is not suitable for infrared thermography, and in what manner affects the data obtained from the monitoring devices.

Software Tool for the Extrinsic Calibration of Infrared and RGBD Cameras Applied to Thermographic Inspection

Context: Thermographic inspections are currently used to assess energy efficiency in electrical equipment and civil structures or to detect failures in cooling systems and electrical or electronic devices. However, thermal images lack texture details, which does not allow for a precise identification of the geometry of the scene or the objects in it. Method: In this work, the development of the software tool called DepTherm is described. This tool allows performing intrinsic and extrinsic calibration between infrared and RGBD cameras in order to fuse thermal, RGB, and RGBD images, as well as to record thermal and depth data. Additional features include user management, a visualization GUI for all three types of images, database storage, and report generation. Results: In addition to the integration tests performed to validate the functionality of DepTherm, two quantitative tests were conducted in order to evaluate its accuracy. A maximum re-projection error of 1,47±0,64 pixels was found, and the maximum mean error in registering an 11 cm side cube was 4,15 mm. Conclusions: The features of the DepTherm software tool are focused on facilitating thermographic inspections by capturing 3D scene models with thermal data.

Active thermography inspection of CFRP using cyclic heating and Fourier transform—comparison with flash heating method

Active thermography nondestructive inspection using cyclic heating and phase images obtained via the Fourier transform is performed for carbon fiber-reinforced plastic (CFRP) specimens, and the defect detection capability of this method is compared with that of the conventional thermography method using flash heating. The experimental results indicate that the defect detection capability is significantly improved when the frequency of the phase image is equal to the cyclic heating frequency. Compared with the flash heating method, a higher signal-to-noise ratio is observed in the phase images obtained via the cyclic heating method, although the temperature increase of the inspection object during heating is lower than that observed during flash heating. These results suggest that the proposed inspection method is effective for CFRPs and other resin-based materials.

Active 3-D Thermography Based on Feature-Free Registration of Thermogram Sequence and 3-D Shape Via a Single Thermal Camera

Active three-dimensional(3-D) thermography combines 3-D shape and active thermography. Thus, it enables the provision of the intuitive thermographic inspection results for composite parts with a complex geometry. However, conventional 3-D thermography acquires thermographic information and 3-D shape through at least two independent sensors and requires complex cross-modal image registration algorithm based on the keypoint detection and matching. In this article, an active 3-D thermography system is proposed using only one thermal camera for moving objects. This system does not require an independent 3-D sensor while the thermal camera acts as the 3-D sensor. Nature behind that is that a mathematical model is proposed to unify the line scanning thermography and laser triangulation in the dynamic scanning process, taking a line laser as both heat excitation for active thermography and spatial coding for 3-D reconstruction. Furthermore, the model enables the feature-free registration of thermogram sequence and 3-D shape, so that the registration is fast and robust without keypoint features. The experiments on standard height specimens, 3-D printing glass fiber composites, and carbon fiber intake tube have shown the error is calibrated within 0.25 mm in the range of 1 to 150 mm and evidenced the capability for subsurface defects detection. The ease and robustness of the proposed active 3-D thermography have a bright future for 3-D measurement, defects detection, and quality control in the production line.

Cost-Effective Flight Strategy for Aerial Thermography Inspection of Photovoltaic Power Plants

Thermography from unmanned aerial vehicles (UAV) is widely used for module condition surveys and defect detection in solar (photovoltaic) power plants. This article presents an optimized defect inspection procedure in which a two-stage autonomous flight strategy is adopted to reduce the operation time, and thereby the cost. The first stage is a fast high-altitude flight for rapid coverage of the entire plant. The resolution on the modules is then somewhat degraded, but adequate for detection of possible defect locations. In the second stage, an optimized flight path is calculated to revisit and image only the detected locations from a lower altitude, where the resolution is sufficient for the classification of defects. This concept is studied through simulations of different plant geometries and defect densities. The simulations are supported by actual data, including a plant-scale survey. The proposed strategy is shown to have the potential for significant savings in operation time, on the order of 60% in the experimental case with 2% module defects. Commonly reported defect densities of 0.5% to 1% will give even larger savings. The inspection strategy is shown to be especially beneficial for plant geometries representative of high latitudes.

Depth classification of defects in composite materials by long-pulsed thermography and blind linear unmixing

This paper presents the automatic analysis of surface thermograms in response to a long-pulsed thermography inspection to classify buried defects in composite materials. Time-dependent thermal contrasts, captured from the sample surface by an infrared thermal camera, are linearly unmixed at the pixel scale to produce results independent of the in-plane defect shapes in the training dataset. The extended blind end-member and abundance extraction (EBEAE) method unmix the thermograms to compute feature vectors carrying information about the internal structure of the composite. The estimated abundances fed an optimized support vector machine (SVM) classifier, which learns a model from the data and labels the defects accordingly to their depths. The inspection of a calibrated glass fiber reinforced polymer proves the ability of EBEAE and SVM in defect classification with an average balanced accuracy of 96.18% in testing. This methodology clearly improves the current state of the art, even without the need for inspections with different source excitations. Furthermore, the estimated end-members automatically model the thermal response of the surface, providing crucial feedback for experimental optimization.

Conducting Thermographic Inspections in Electrical Substations: A Survey

Liberalization of the electricity market has forced the use of economic and efficient maintenance techniques. Thus, it is necessary to extend the useful life of the facilities in a perfect state of service, and with quality supply for customers. Furthermore, thermography is a maintenance technique that can be implemented quickly, has low cost, and is very effective in terms of the results provided. For this reason, it is very widespread within the electricity sector. As substations are critical facilities within the electrical supply, thermographic inspections are carried out on them very frequently. However, to ensure that the results obtained are reliable, a series of requirements must be met. A compilation of the complete process of the performance of a correct thermography in substations is shown. The factors that affect performing a correct thermographic inspection are indicated. These can be procedural, technical, and environmental. In addition, conditions for conducting thermographic inspections and the action to performed on a hot spot are indicated. The hot spot is usually identified with a current and wind speed other than nominal; these two variables can mask the true severity of the hot spot. For this reason, the extrapolation of the detected temperature to the nominal current conditions and in the absence of wind is carried out using a proposed formula. Finally, two examples of application of the proposed formula and the recommended action on them are exposed.

Dual-IRT-GAN: A defect-aware deep adversarial network to perform super-resolution tasks in infrared thermographic inspection

InfraRed Thermography (IRT) is a valuable diagnostic tool for detecting defects in fiber-reinforced polymers in a non-destructive manner through the measurement of surface temperature distribution. Yet, thermal cameras typically have a low native spatial resolution resulting in a blurry and low-quality thermal image sequence.This study proposes a defect-aware Generative Adversarial Network (GAN) framework, termed Dual-IRT-GAN, in order to simultaneously perform Super-Resolution (SR) and defect detection tasks in infrared thermography. Furthermore, the visibility of defective regions in generated high-resolution images are enhanced by leveraging defect-aware attention maps from segmented defect images.Following a series of augmentation techniques and a second-order degradation process, the proposed Dual-IRT-GAN model is trained on an extensive numerically generated thermographic dataset of composite materials with various defect types, sizes and depts. The high inference performance of the virtually trained Dual-IRT-GAN is demonstrated on several experimental thermographic datasets which were obtained from composite coupon specimens with various defect types, sizes, and depths, as well as from aircraft stiffened composite panels having real (production) defects.

Enabling thermography of highly reflective laser welds using a multi-flash sequence approach

A multi-flash thermography approach was developed to enable thermographic inspection of highly reflective samples without the need for an emissivity-altering coating. The multi-flash thermography approach was able to reliably remove reflections and isolate the weld region of interest. Properly isolating the weld region of interest enables weld assessment for highly reflective metal welded joints, such as laser welds. Future work includes exploring various techniques to both identify the weld as well as analyze the weld quality via machine learning approaches.

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.