Thermography Research Articles

SunMap: Towards Unattended Maintenance of Photovoltaic Plants Using Drone Photogrammetry

Global awareness of environmental issues has boosted interest in renewable energy resources, among which solar energy is one of the most attractive renewable sources. The massive growth of PV plants, both in number and size, has motivated the development of new approaches for their inspection and monitoring. In this paper, a rigorous drone photogrammetry approach using optical Red, Green and Blue (RGB) and Infrared Thermography (IRT) images is applied to detect one of the most common faults (hot spots) in photovoltaic (PV) plants. The latest advances in photogrammetry and computer vision (i.e., Structure from Motion (SfM) and multiview stereo (MVS)), together with advanced and robust analysis of IRT images, are the main elements of the proposed methodology. We developed an in-house software application, SunMap, that allows automatic, accurate, and reliable detection of hot spots on PV panels. Along with the identification and geolocation of malfunctioning PV panels, SunMap provides high-quality cartographic products by means of 3D models and true orthophotos that provide additional support for maintenance operations. Validation of SunMap was performed in two different PV plants located in Spain, generating positive results in the detection and geolocation of anomalies with an error incidence lower than 15% as validated by the manufacturer’s standard electrical tests.

Very High spatial Resolution IR thermography diagnostic positioning system upgrade in WEST Tokamak

The Very High spatial Resolution (VHR) infrared thermography diagnostic developed by CEA-IRFM is installed in the WEST tokamak to measure surface temperature of the actively cooled W-monoblocks (MB) of the Plasma Facing Units (PFUs), which are similar to the International Thermonuclear Experimental Reactor (ITER) ones. It is a major diagnostic to investigate the behavior of PFU during experimentation thanks to its very high spatial resolution (0.1 mm / pixel), movable field of view (FoV) and the wide-range temperature measurement. In particular, it can study the evolution of monoblocks mounted in the tokamak, the influence of the shaping of these components submitted to high heat load and the behavior of the leading edges regarding the assembling tolerances between adjacent monoblocks. Finally, such diagnostic directly contributes to the specification assessment of the ITER divertor units. This diagnostic was operational during the 2019 WEST experimentation and its enhanced design for the 2020 WEST experimentation campaign. In order to monitor 30 × 12 mm monoblocks temperature, the VHR IR diagnostic is able to move its narrow FoV (64 × 51 mm) to specific targets on the observable divertor area (360 × 420 mm). Position and focus steering are achieved through motor-controlled mirrors and lenses operated by a dedicated Labview application on a remote unit. Mirror positioning occurs between pulses under normal WEST operation conditions, and can withstand thermal radiation and disruptions during pulses. Repeatability, hysteresis, mechanical components reliability are critical parameters to provide accurate positioning (<1 mm) of the FoV. The tuning and calibration of these actuators in accordance with WEST environment is crucial to guarantee the best position of the FoV and optical performances. This paper gives the FoV positioning implementation details and performance of the VHR in WEST.

Infrared thermography method to detect cracking of nuclear fuels in real-time

The efficiency and performance of nuclear fuels are essential to the safety, reliability, and economics of nuclear energy. Crack formation occurs relatively early in the fuel operation cycle and lowers its life and efficiency; accordingly, understanding crack formation and propagation in fuels is key to evaluating and improving fuel performance. The harsh environment inside a nuclear reactor is the primary challenge for real-time fuel monitoring. A coherent fiber optic bundle (CFOB) based infrared thermography (IRT) method may be a viable method for real-time fuel monitoring. A radiation hardened, temperature resistant CFOB can be used to transmit a real-time image from inside the harsh reactor environment to the near infrared (NIR) camera outside the reactor. In this paper, the feasibility of the method was tested and confirmed on oxide nuclear fuel surrogates in a laboratory setting. Two different fuel surrogate materials are showcased with and without the CFOB using furnace heating, laser heating, and reflected illumination from an incandescent bulb. The behavior of common features, such as cracks, mounds and pits, were all compared in detail for the different heating/illumination techniques and samples for temperatures ranging from 23–500°C. The effects of five different image processing techniques were also studied. Cracks down to ≈300nm wide with a field of view of ≈6.4×5.12mm were easily detected with the IRT system with and without the CFOB.

NDT spatial data integration for monumental buildings: technical information management for the Royal Alcazar of Seville

ABSTRACT Non-destructive testing (NDT) for the diagnosis of heritage buildings has proved to be one of the most sustainable approaches in the field of preventive conservation. Nevertheless, the comprehensive management of NDT information is complex, often resulting in a poorly optimized use of the data. This contribution aims to propose a method for integrating NDT data in a standardized way into simplified digital information models, produced using Geographic Information Systems (GIS). This method is applied to the plasterwork located in the Courtyard of the Maidens, which are elements with an enormous historic-artistic value in the Royal Alcazar of Seville (Spain), a significant UNESCO monument. For this purpose, a multi-scale web GIS-based model has integrated the NDT information from the plasterworks, such as ambient conditions, damage mapping, C-Shore Hardness, Superficial Humidity, Infrared thermography (IRT), Digital Image Processing (DIP) and Ground Penetration Radar (GPR). The combined georeferencing of the data from the various measurements and their simultaneous visualization has enabled the detection of vulnerable areas in the complex. The results are easily accessible for managers, and additionally show the analytical potential of the research approach, thereby allowing a quick diagnosis and hierarchization of tasks.

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.

Infrared Thermography Compared to Standard Care in the Prevention and Care of Diabetic Foot: A Cost Analysis Utilizing Real-World Data and an Expert Panel.

Infrared thermography (IRT) is a non-invasive technology for screening and early detection of diabetic foot. Real-world data and the Delphi technique were used to assess IRT's potential effect on typical care pathways of diabetic foot and their costs in the Finnish healthcare setting. The most typical care pathways of diabetic foot were identified from national healthcare registers from 2011 to 2017. The effect of IRT in terms of avoidable care episodes was assessed by a Delphi panel including Finnish diabetic foot specialists (n=13). By combining a series of decision-analytic models, the IRT's potential effect on the costs of each pathway and their sensitivity to model assumptions were estimated. Hypothetical annual savings were estimated to be EUR ~1.7 million (EUR ~1.3 million-EUR ~2.5 million), constituting approximately 20% of the total annual care pathway costs examined. In the longer and more complex pathways, the application of IRT was estimated to result in notable savings while in the shorter pathways, IRT could increase costs. Our modeling suggests that IRT could potentially reduce costs in a Finnish healthcare setting. Given our analysis, generation of robust evidence on the effectiveness of recent IRT technologies with up-to-date protocols seems appropriate.

A highly active mineral-based ice nucleating agent supports in situ cell cryopreservation in a high throughput format.

Cryopreservation of biological matter in microlitre scale volumes of liquid would be useful for a range of applications. At present, it is challenging because small volumes of water tend to supercool, and deep supercooling is known to lead to poor post-thaw cell viability. Here, we show that a mineral ice nucleator can almost eliminate supercooling in 100 µl liquid volumes during cryopreservation. This strategy of eliminating supercooling greatly enhances cell viability relative to cryopreservation protocols with uncontrolled ice nucleation. Using infrared thermography, we demonstrate a direct relationship between the extent of supercooling and post-thaw cell viability. Using a mineral nucleator delivery system, we open the door to the routine cryopreservation of mammalian cells in multiwell plates for applications such as high throughput toxicology testing of pharmaceutical products and regenerative medicine.

Siamese Cross-Domain Tracker Design for Seamless Tracking of Targets in RGB and Thermal Videos

Multimodal (RGB and thermal) applications are swiftly gaining importance in the computer vision community with advancements in self-driving cars, robotics, Internet of Things, and surveillance applications. Both the modalities have complementary performance depending on illumination constraints. Hence, a judicious combination of both modalities will result in robust RGBT systems capable of all-day all-weather applications. Several studies have been proposed in the literature for integrating the multimodal sensor data for object tracking applications. Most of the proposed networks try to delineate the information into modality-specific and modality shared features and attempt to exploit the modality shared features in enhancing the modality specific information. In this work, we propose a novel perspective to this problem using a Siamese inspired network architecture. We design a custom Siamese cross-domain tracker architecture and fuse it with a mean shift tracker to drastically reduce the computational complexity. We also propose a constant false alarm rate inspired coasting architecture to cater for real-time track loss scenarios. The proposed method presents a complete and robust solution for object tracking across domains with seamless track handover for all-day all-weather operation. The algorithm is successfully implemented on a Jetson-Nano, the smallest graphics processing unit (GPU) board offered by NVIDIA Corporation.

Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

To support the independent living of older adults in their own homes, it is essential to identify their abnormal behaviors before triggering an automated alert system. Existing normal vision sensing approaches to detect human falls in the activities of daily living (ADL) experienced acceptability issues due to outstanding privacy concerns when they are deployed in personal environments. Besides, false alerts (false-positive) fall detection has not been addressed thoroughly in systems that report abnormal human behaviors as emergency alerts to the information support. This article proposes a novel human-in-the-loop fall detection approach in the ADLs using a low-resolution thermal sensor array. The motivation for enabling a human interactive model, fall detection confirmation, is to influence resource efficiency by reducing false-positive alerts while keeping the false-negative fall predictions as low as possible. The proposed approach is based on the motion sequence classification of human movements using a recurrent neural network. The proposed approach is evaluated with comprehensive experiments using different learning techniques, users, and domestic environment conditions. This article shows a performance accuracy of 99.7% to detect human falls from various typical ADLs.

Intraoperative thermal infrared imaging in neurosurgery: machine learning approaches for advanced segmentation of tumors

Surgical resection is one of the most relevant practices in neurosurgery. Finding the correct surgical extent of the tumor is a key question and so far several techniques have been employed to assist the neurosurgeon in preserving the maximum amount of healthy tissue. Some of these methods are invasive for patients, not always allowing high precision in the detection of the tumor area. The aim of this study is to overcome these limitations, developing machine learning based models, relying on features obtained from a contactless and non-invasive technique, the thermal infrared (IR) imaging. The thermal IR videos of thirteen patients with heterogeneous tumors were recorded in the intraoperative context. Time (TD)- and frequency (FD)-domain features were extracted and fed different machine learning models. Models relying on FD features have proven to be the best solutions for the optimal detection of the tumor area (Average Accuracy = 90.45%; Average Sensitivity = 84.64%; Average Specificity = 93,74%). The obtained results highlight the possibility to accurately detect the tumor lesion boundary with a completely non-invasive, contactless, and portable technology, revealing thermal IR imaging as a very promising tool for the neurosurgeon.

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.

The ENBIS‐21 quality and reliability engineering international special issue

The ENBIS-21 Quality and Reliability Engineering International Special Issue is related to the contributions to the 21st Annual Conference of the European Network for Business and Industrial Statistics (ENBIS) held online from 13 to 15 September, 2021. The topics covered by the 12 papers included in this special issue fall within the scope of ENBIS, whose ultimate goal is to bridge the gap between methodology and application in the field of business and industrial statistics and foster networking between statisticians in academic and commercial settings. It becomes evident from the papers on this issue that classical methods of industrial statistics are still having a wide range of applications and expansions towards complex and big data structures as well as they are increasingly cross-fertilized by artificial intelligence and machine learning perspectives and approaches. The issue is opened by the contribution of Yang et al.1 in the Statistical Process Monitoring (SPM) of data in the form of tensors. The authors develop a hierarchical in-situ process monitoring approach for anomaly detection, which is applied to thermal video streams acquired during a metal additive manufacturing process for highly complex parts. A more traditional SPM approach based on generalized likelihood ratio (GLR) control charts is elaborated by Rizzo and Di Bucchianico2 to set up tailor-made monitoring for high-purity (high-quality) processes and deals with issues arising in GLR control charts for discrete processes. Along the line of developing statistical methods for complex data structures, Gril et al.3 set up a tensor-on-tensor regression model able to dynamically predict repetitive human motions helpful in avoiding unwanted collisions at the interface with robots in industrial assembly tasks. Chuquin et al.4 present a straightforward extension of the K-means clustering to spatially correlated functional data, with an application in the analysis of the normalized difference vegetation index in a large region of Ecuador. From a more general perspective, Vanacore et al.5 address the problem of assessing prediction performance in the multi-class classification of imbalanced data sets. In the optimal Design of Experiments (DoE) Pesce et al.6 offer new results that are oriented to bias reduction in large datasets and protection against confounders. Whereas, Kirchhoff et al.7 use DoE as the initial solution for the optimization of shift planning in high-bay warehouse operations based on Gaussian process models with low-rank correlation kernels. Risk assessment and management is the topic of the following three papers. Testik and Unlu8 compare traditional and fuzzy failure modes and effect analysis to rank risks in different areas of testing and calibration laboratories. Meynaoui et al.9 deal with an accidental scenario in a sodium-cooled fast nuclear reactor by means of a global sensitivity analysis of the input parameter distribution used in numerical simulator outputs to model physical phenomena. Lai and Zwetsloot10 offer a data-driven ensemble ranking system of product quality, which is validated in the identification of high-risk manufacturers in a solar industry case study. The final two papers are devoted to the Reliability and Maintenance of repairable systems. El-Aroui and Gaudoin11 propose a general goodness-of-fit test for imperfect repair models that has the favorable property of being asymptotically distribution-free for renewal and power law processes. Fouladirad et al.12 develop a new bounded transformed gamma process model to describe and predict degradation phenomena by relaxing the typical assumption that the degradation level can grow indefinitely, which is often unrealistic for technological units. The proposed model is also applied to a set of wear measures of cylinder liners that equip a diesel engine for marine propulsion. We are deeply grateful to the journal staff for their professional assistance in the editorial process, as well as to the anonymous referees for the time and effort spent to provide careful and constructive comments, and to all the authors, who made revisions on a very tight timescale.

On the Trail of Some Human Heat. Thermal Imaging, Visual Culture and the Covid-19 Pandemic

The paper is divided into two main aims. In the first part, it wants to investigate the presence of thermal imaging in the context of contemporary visual culture, especially during the Covid-19 pandemic, when thermal imaging widely demonstrated how currently optics models of vision are increasingly accompanied by thermal models of visualisation. The result of the latter is thermography: at the same time, a process that transforms our epistemic relationship with reality, extending the possibilities of visualising information beyond the limits of the visible spectrum; and an image that maintains the indexical bound with its referent, but that is conceived as the outcome of an elaborated and displayed dataset. In the second part, the paper focuses on the artistic field in which thermal vision gained further relevance in the context of the pandemic. On the one hand, the artists have criticised and deconstructed it; on the other hand, they have relaunched its use in a biopolitical horizon, such as in the case of the photographic project Virus (2020), in which Antoine d’Agata roamed the deserted streets of Paris and visited some intensive care units with a thermal camera, during the first national lockdown in 2020.

Experimental study on the temperature measurement of melt blowing fibers through use of infrared thermography

ABSTRACT The temperature of melt blowing (MB) and melt spun fibers is frequently measured using infrared thermography (IRT). However, measurements are affected by the size and curvature of the fiber and by reflected radiation from the nozzle. A procedure was developed to obtain accurate measurements of fiber temperature, which corrects fiber size, fiber curvature, and reflected radiation. This procedure is more general than the ones used so far to correct fiber temperature because it corrects the three abovementioned factors using a single correction formula, this being the first time that fiber temperature correction for reflected radiation has been accounted for. It is also more accurate because it uses the local fiber diameter to correct fiber curvature, instead of mean fiber diameter. The procedure was used to measure the temperature of MB adhesive fibers produced by two multi-hole nozzle designs, the results indicating that, in melt blowing applications, the distortion caused by fiber curvature and reflected radiation on the measurement proves to be far more significant than that caused by fiber size, while correction generally leads to an increase in fiber temperature. Considerations as to the effect of different parameters on temperature correction, as well as the quality of thermographic images, are also provided throughout the text. The conclusions emerging from this study allow more accurate temperature measurements of melt blown and melt spun fibers to be obtained.

Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography.

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented.

Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme.

The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module's operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.

Field Inspection of High-Density Polyethylene (HDPE) Storage Tanks Using Infrared Thermography and Ultrasonic Methods

High-density polyethylene (HDPE) is widely used for above-ground storage tanks (ASTs). However, there are currently no guidelines for the non-destructive testing (NDT) and evaluation (NDE) of HDPE ASTs. Moreover, the feasibility, limitations, and challenges of using NDT techniques for the field inspection of HDPE ASTs have not been well established. This study used both infrared thermography (IRT) and ultrasonic testing (UT) for the field inspection of HDPE ASTs. Highlighting the implementation challenges in the field, this study determined that: (1) ambient environmental parameters can affect IRT accuracy; (2) there is an ideal time during the day to perform IRT; (3) the heating source and infrared camera orientation can affect IRT accuracy; and (4) with proper measures taken, IRT is a promising method for flaw detection in HDPE ASTs. Additionally, UT can be used following IRT for detailed investigation to quantify the size and depth of defects. The manuscript concludes with a discussion of the limitations and best practices for the implementing of IRT and UT for HDPE AST inspections in the field.

Improving the Heat Transfer of Phase Change Composites for Thermal Energy Storage by Adding Copper: Preparation and Thermal Properties

Phase change materials (PCMs), as an effective thermal energy storage technology, provide a viable approach to harness solar heat, a green energy source, and optimize energy consumption in buildings. However, the obstacle preventing widespread practical use of PCM is its poor performance in terms of heat transfer and shape stabilization. This article focuses on the application of the shape stabilization method. To improve the thermal conductivity of organic PCMs (hexadecane), copper microparticles are added to form phase change composites (PCC). This process allows an enhanced PCM (75 wt%) that distributes effective thermal storage capabilities while maintaining low cost. SEM, FTIR, ATG, infrared thermography (IRT), and DSC were used to characterize the composites’ micromorphology, chemical composition, thermal degradation stability, and thermal energy storage capabilities. DSC results showed that a proportion of 75 wt% phase change material with 15 wt% Cu had excellent thermal stability and high energy storage density per unit mass. In light of its high latent heat storage capacity of 201.32 J/g as well as its ability to prevent Hexadecane exudation, PCC ensures higher thermal conductivity and shape stability during phase transition than ordinary PCM. The incorporation of Cu to paraffin causes delay in PCM phase transformation, leading it to respond to rapid charging and discharging rates and, consequentially, to challenges in temperature control, as shown by IRT. The new PCCs had favorable thermal stability below 100 °C, which was advantageous for practical application for thermal energy storage and management, and notably for solar thermal energy storage.

Heat transfer in aluminum-steel joint and weld tool during the friction stir welding: Simulation and experimental validation

In this work, dissimilar butt joints of 6061 aluminum and 304 stainless steel were prepared by friction stir welding (FSW), and the FSW process is monitored by the infrared (IR) thermography. Considering the different material properties between aluminum and steel, a novel CFD model with weld tool is developed and tested to investigate the effects of weld tool on the aluminum-steel joint under different traverse speeds. The calculated temperature and thermal cycles for various traverse speed agreed well with the corresponding IR thermography experimental results. It is found that the calculated peak temperature is about 10 K lower than measured peak temperature and the relative error is 2.3%, while it is 11.4% for the model without weld tool. The model with weld tool can decrease the temperature gradient and inhomogeneity of temperature field for aluminum-steel joint. SEM analysis of the interface between aluminum and steel showed that the thickness of IMCs may decrease with traverse speed. The model with weld tool can provide calculated viscosity based on the validated temperature field to analyze the insufficient stirring defect of aluminum-steel joint.

Effects of strain rate and adiabatic heating on mechanical behavior of medium manganese Q&P steels

In this work, the mechanical behavior and properties of four different multiphase steels was studied in tension at strain rates of 10−4, 10−2, 0.5 and 800 s−1. The four materials include a medium manganese (3%) steel grade overcritically and intercritically annealed and Q&P heat treated and two industrially produced TRIP-assisted steels, DH800 and TRIP700 steels, which have different retained austenite morphology. The temperature and strain of the specimens were studied using high speed infrared thermography (IRT) and digital image correlation (DIC). The mechanical response of the Q&P steels had considerably higher tensile strength than the two industrially produced steels. The Q&P steel with a higher austenite volume fraction strain hardened significantly more than the other steels. The DH800 steel and the intercritically annealed Q&P steel heated less with ΔT of 25 °C during uniform deformation than the TRIP700 steel and the overcritically annealed Q&P steel with ΔT of 35 °C. However, the industrially produced steels DH800 and TRIP700 had higher uniform elongation of 0.12 mm/mm and 0.14 mm/mm whereas the Q&P steels reached only 0.09 mm/mm, meaning that the heating rate of the Q&P steels was considerably steeper. In addition, the stronger necking of the DH800 and TRIP700 steels led to much higher maximum temperatures before failure (max. 260 °C) than those observed for the Q&P steels (max. 140 °C). The Taylor-Quinney coefficients of the Q&P steels were large in the beginning of the plastic deformation (0.65–0.95) but decreased as a function of plastic deformation, whereas the Taylor–Quinney Coefficients of the DH800 and TRIP700 steels were lower (0.5–0.6) but increased gradually as a function of plastic deformation.