Search Results (9)

Solar photovoltaic (SPV) arrays play a pivotal role in advancing clean and sustainable energy systems, with a worldwide total installed capacity of 1.6 terawatts and annual investments reaching USD 480 billion in 2023. However, climate disaster effects, particularly extremely hot weather events, can compromise the performance and resilience of SPV panels through thermal deterioration and degradation, which may lead to lessened operational life and potential failure. These heatwave-related consequences highlight the need for timely inspection and precise anomaly diagnosis of SPV panels to ensure optimal energy production. This case study focuses on intelligent remote inspection by employing aerial radiometric infrared thermography within a predictive maintenance framework to enhance diagnostic monitoring and early scrutiny capabilities for SPV power plant sites. The proposed methodology leverages pre-trained deep learning (DL) algorithms, enabling a deep transfer learning approach, to test the effectiveness of multiclass classification (or diagnosis) of various thermal anomalies of the SPV panel. This case study adopted a highly imbalanced 6-class thermographic radiometric dataset (floating-point temperature numerical values in degrees Celsius) for training and validating the pre-trained DL predictive classification models and comparing them with a customized convolutional neural network (CNN) ensembled model. The performance metrics demonstrate that among selected pre-trained DL models, the MobileNetV2 exhibits the highest F1 score (0.998) and accuracy (0.998), followed by InceptionV3 and VGG16, which recorded an F1 score of 0.997 and an accuracy of 0.998 in performing the smart inspection of 6-class thermal anomalies, whereas the customized CNN ensembled model achieved both a perfect F1 score (1.000) and accuracy (1.000). Furthermore, to create trust in the intelligent inspection system, we investigated the pre-trained DL predictive classification models using perceptive explainability to display the most discriminative data features, and mathematical-structure-based interpretability to portray multiclass feature clustering. Full article

Pain management requires the identification of certain indicators to recognize pain. Various tools have been suggested to achieve an objective evaluation, including infrared thermography (IRT). The objective of this study was to assess the facial thermal nociceptive response produced by the use of cannabidiol (CBD) alone and in combination with meloxicam in female dogs undergoing elective ovariohysterectomy anesthetized with isoflurane. Sixty-four female dogs of different breeds were randomly distributed into four study groups according to the treatment received. G1: Placebo group (n = 16); G2: Group receiving intravenous meloxicam as premedication (0.2 mg Kg⁻¹) and every 24 h postoperatively 0.1 mg Kg⁻¹ (n = 16); G3: Group treated with CBD (n = 16) at a dose of 2 mg kg⁻¹ orally every 12 h; and G4: Group medicated with the combination of both treatments (n = 16). All treatments were administered for 48 h postoperatively. After the anesthetic surgical procedure, radiometric images were captured using IRT and physiological parameters during the events E_Basal, E_30min, E_1h, E_2h, E_3h, E_4h, E_8h, E_12h, E_24h and E_48h. Overall, it was found that the high, medium and low temperatures of the thermal windows of the eye, upper eyelid and lower eyelid, as well as the average temperature of the lacrimal gland in G1 between events, were significantly lower at E_30min, E_1h and E_2h compared to E_Basal (p = 0.01). Among treatments, a significantly higher temperature was observed in groups G2, G3 and G4 compared to G1 (p = 0.001) in the thermal windows of the upper eyelid, lower eyelid, lacrimal gland and ocular areas. Regarding physiological parameters, heart rate (HR) was higher in G1 compared to the animals in G2, G3 and G4 (p = 0.03). The respiratory rate (RR) was significantly lower in all four study groups during the postoperative events compared to their respective E_Basal (p < 0.05), while among treatments, G2, G3 and G4 had a lower RR compared to G1 (p = 0.03). Mild hypothermia was observed in all study groups at E_30min and E_1h compared to E_Basal (p = 0.001). No significant correlation was found between the temperatures of the assessed thermal regions and the physiological traits. In conclusion, CBD, whether administered alone or in combination with meloxicam, demonstrated comparable analgesic efficacy, which could control nociceptive cardiorespiratory and hemodynamic autonomic responses, as there were no significant changes in the facial thermal response between treatments G2, G3 and G4. Full article

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering. Full article

In the thermography process, accurately determining emissivity is crucial to obtain precise temperature measurements as it enables the conversion of radiometric values to absolute temperatures. However, assessing emissivity is not a straightforward task as it depends on various other parameters. Traditional methods for measuring emissivity often involve costly materials and cannot be carried out simultaneously with infrared image acquisition. This article presents a method for obtaining pixel-wise emissivity using data from a multispectral infrared camera. Consequently, this method allows for direct emissivity measurement during infrared camera acquisition without the need for additional materials or experiments. Full article

Thermography is probably the most used method of measuring surface temperature by analyzing radiation in the infrared part of the spectrum which accuracy depends on factors such as emissivity and reflected radiation. Contrary to popular belief that thermographic images represent temperature maps, they are actually thermal radiation converted into an image, and if not properly calibrated, they show incorrect temperatures. The objective of this study is to analyze commonly used image processing techniques and their impact on radiometric data in thermography. In particular, the extent to which a thermograph can be considered as an image and how image processing affects radiometric data. Three analyzes are presented in the paper. The first one examines how image processing techniques, such as contrast and brightness, affect physical reality and its representation in thermographic imaging. The second analysis examines the effects of JPEG compression on radiometric data and how degradation of the data varies with the compression parameters. The third analysis aims to determine the optimal resolution increase required to minimize the effects of compression on the radiometric data. The output from an IR camera in CSV format was used for these analyses, and compared to images from the manufacturer’s software. The IR camera providing data in JPEG format was used, and the data included thermographic images, visible images, and a matrix of thermal radiation data. The study was verified with a reference blackbody radiation set at 60 ${}^{°}$C. The results highlight the dangers of interpreting thermographic images as temperature maps without considering the underlying radiometric data which can be affected by image processing and compression. The paper concludes with the importance of accurate and precise thermographic analysis for reliable temperature measurement. Full article

Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster inspection. This paper presents a 3D reconstruction method associated with temperature information. The proposed solution is based on a Structure from Motion and Multi-view Stereo approach, exploiting a set of multimodal merged images. The infrared images were obtained by automatically processing the radiometric data to remove thermal interferences, segment the RoI, enhance false-color contrast, and for multimodal co-registration under a controlled environment and a ∆T < 2.6% between the RoI and thermal interferences. The geometric verification accuracy was 77% ± 2%. Moreover, a normalized error was adjusted per sample based on a linear model to compensate for the curvature emissivity (error ≈ 10% near to 90°). The 3D models were displayed with temperature information and interaction controls to observe any point of view. The temperature sidebar values were assigned with information retrieved only from the RoI. The results have proven the feasibility of the 3D multimodal construction to be used as a promising tool in the diagnosis of diabetic foot. Full article

Engineering graphics are present in the design stage, but also constitute a way to communicate, analyse, and synthesise. In the Architecture-Engineering-Construction sector, graphical data become essential in analysing buildings and constructions throughout their lifecycles, such as in the thermal behaviour assessment of building envelopes. Scientific research has addressed the thermal image mapping onto three-dimensional (3D) models for visualisation and analysis. However, the 3D point cloud data creation of buildings’ thermal behaviour directly from rectified infrared thermography (IRT) thermograms is yet to be investigated. Therefore, this paper develops an open-source software graphical method to produce 3D thermal data from IRT images for temperature visualisation and subsequent analysis. This low-cost approach uses both a geographic information system for the thermographic image rectification and the point clouds production, and 3D point cloud processing software. The methodology has been proven useful to obtain, without perspective distortions, 3D thermograms even from non-radiometric raster images. The results also revealed that non-rectangular thermograms enable over 95% of the 3D thermal data generated from IRT against rectangular shapes (over 85%). Finally, the 3D thermal data produced allow further thermal behaviour assessment, including calculating the object’s heat loss and thermal transmittance for diverse applications such as energy audits, restoration, monitoring, or product quality control. Full article

Infrared thermography (IRT) techniques for building inspection are currently becoming increasingly popular as non-destructive methods that provide valuable information about surface temperature (ST) and ST contrast (delta-T). With the advent of unmanned aerial vehicle (UAV)-mounted thermal cameras, IRT technology is now endowed with improved flexibility from an aerial perspective for the study of building envelopes. A case study cellar in Northwest (NW) Spain is used to assess the capability and reliability of low-altitude passive IRT in evaluating a typical semi-buried building. The study comparatively assesses the use of a pole-mounted FLIR B335 camera and a drone-mounted FLIR Vue Pro R camera for this purpose. Both tested IRT systems demonstrate good effectiveness in detecting thermal anomalies (e.g., thermal bridges, air leakages, constructive singularities, and moisture in the walls of the cellar) but pose some difficulties in performing accurate ST measurements under real operating conditions. Working with UAVs gives great flexibility for the inspection, but the angle of view strongly influences the radiometric data captured and must be taken into account to avoid disturbances due to specular reflections. Full article

A new 3D acquisition and processing procedure to map RGB, thermal IR and near infrared images (NIR) on a detailed 3D model of a building is presented. The combination and fusion of different data sources allows the generation of 3D thermal data useful for different purposes such as localization, visualization, and analysis of anomalies in contemporary architecture. The classic approach, which is currently used to map IR images on 3D models, is based on the direct registration of each single image by using space resection or homography. This approach is largely time consuming and in many cases suffers from poor object texture. To overcome these drawbacks, a “bi-camera” system coupling a thermal IR camera to a RGB camera has been setup. The second sensor is used to orient the “bi-camera” through a photogrammetric network also including free-handled camera stations to strengthen the block geometry. In many cases the bundle adjustment can be executed through a procedure for automatic extraction of tie points. Terrestrial laser scanning is adopted to retrieve the 3D model building. The integration of a low-cost NIR camera accumulates further radiometric information on the final 3D model. The use of such a sensor has not been exploited until now to assess the conservation state of buildings. Here some interesting findings from this kind of analysis are reported. The paper shows the methodology and its experimental application to a couple of buildings in the main Campus of Politecnico di Milano University, where IR thermography has previously been carried out for conservation and maintenance purposes. Full article