Infrared Thermography Imaging Research Articles

Condition Assessment of Electrical Power Devices using Infrared Thermography: A Review

Departures from the stable operation of electrical equipment usually appear as changes in parameters such as vibrations, pressure, temperature, power, and flow. Excessive changes in these parameters will indicate significant damage, leading to total or partial breakdown of equipment. This paper reviews the use of infrared thermography (IRT) for the operating condition temperature monitoring of electrical equipment. Infrared thermography is a contactless method that is based on thermal radiation principles and used for determining the temperature distribution on the surface of objects by measuring the intensity of radiation emitted by the object. The basic principles underlying IRT are presented in this review, showing its pros and cons, the various IRT methods, processes involved in IRT image acquisition, intelligent techniques for IR image analysis, and factors that affect the accuracy of results, including technical, environmental, and procedural factors. Also presented is an overview of common applications of IRT condition monitoring to solar energy, medicine, civil infrastructure, and aerospace equipment. Common faults in electrical equipment, including those in transformers, circuit breakers, feeder pillars and arrestors, are discussed with illustrations from recent research studies. These studies have demonstrated the effectiveness of IRT in the predictive maintenance of electrical installations. Hence, the replication of the techniques reviewed in this work is recommended so as to reduce current costs incurred in maintaining and replacing damaged equipment, and the associated time losses.

A novel application of deep learning approach over IRT images for the automated detection of rising damp on historical masonries

Nowadays, the fusion of Artificial Intelligence (AI) comprises a widespread approach for resolving various types of problems in many scientific domains including Protection of Monuments. Non-Destructive Testing (NDT) approaches and Infra-Red Thermography (IRT) specifically, plays a key role for the diagnosis and the assessment of the monuments’ preservation state. Additionally, IRT comprises a powerful tool for continuous monitoring especially when it concerns the physical and/or chemical processes that take place within or on the material and affect the irradiation of the historical surfaces. This study explores the application of Deep Learning (DL) to IRT images of passive approach, focusing on the automated detection of rising damp in historical masonries. The IRT data were acquired from two monuments, the Holy Aedicule of the Holy Sepulchre and the Historical Building "Msma’a". Exploiting the capabilities of AI for enhancing the non-intrusive nature of passive IRT, this research seeks to provide a cost-effective and non-destructive approach for the early identification of rising damp, contributing significantly to the long-term preservation, conservation, and protection of the cultural heritage. To achieve this, the study takes advantage of a combination of the PSPNet image segmentation model with the ResNet-50 backbone, the PSP_R50 model. The mmsegment framework, renowned for its versatility and effectiveness, serves as the ideal platform for training, evaluating, and fine-tuning the proposed segmentation model. Despite having a relatively small dataset, a highly effective segmentation model (0.93 accuracy, 0.89 IoU), has been successfully developed.

INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC

Soil is the substance most likely to meet nature and dirt people, vehicles, and clothing, especially in outdoor. Both source material and soil samples can be damaged during industrial and criminal investigations. Therefore, there is a need for detection, examination, and identification systems that can minimize contact with forensic evidence and provide accurate results with fewer samples. The study aims to determine the type of soil using a low-cost, easily accessible, and highly sensitive system that can be used easily without interference from the surface properties of the textile or destruction of the structure of the dirt. The working sites and areas of samples to be collected were determined according to the purpose of the study. In this context, samples of the most common soil types were taken from the lands in the Aegean Region of Turkey. Different types of substances were applied and dirtying on the collected samples. The newly formed samples were heated with a heating surface and allowed to cool. During this process, a thermal video was recorded, and feature extraction was performed. 165 samples were obtained from 55 tests. As a result, it is seen that the proposed method can detect samples with 97% accuracy.

Thermal imaging based pre-diagnostics tool for Graves’ disease

Infrared thermography Images of the facial region are taken from sixty persons. Scintigraphy and standard thyroid blood test are used to categorize these persons into thirty-three females and thirteen males suffering from Graves’ disease. This study is approved by the All-India Institute of Medical Science Rishikesh Ethics Committee with reference number AIIMS/IEC/19/997. Eleven Females and three males are found to be in healthy conditions and used as control. A convolutional neural networks (CNN) model is developed to automatically segment and extract the histogram-associated information within the thyroid and cheek region from the collected images. The sub-surface temperature of the thyroid gland and control is extracted using these set of images. We have acquired moderately correlated imaging biomarker with respect to age and gender from this sparse data. An Artificial Intelligence-based app is developed and deployed in a clinical environment to enrich the prognosis model in real time. An affordable Thermal plug-and-play addon device is developed to connect with any smartphone for faster diagnosis at the patient end to carry out this test now. This smartphone and AI-based app combination is successfully deployed as a point-of-care device. It is expected that this IR based preliminary test will automatically categorize healthy cases from patients. This step may save the clinicians to unnecessarily recommending the radioactive contamination-prone Scintigraphy and/or expensive and relatively slower thyroid blood tests. Such preliminary tests may (a) save costs to the patients and (b) relieve the burden on pathology labs. These two points are impactful for the healthcare industry, particularly in densely populated countries having low per capita income.

Material-enabled damage inspection of multifunctional shape memory alloy tufted composite T-joints

The through-the-thickness reinforcement of carbon-epoxy composite joints with shape memory alloy (SMA) tufts has shown significant improvement of the mechanical strength, fracture toughness, and delamination resistance. This study explores the thermal-electric properties of SMA filaments tufted in composite T-joints to exhibit multiple functionalities including material-enabled thermographic inspection and structural health monitoring via in-situ strain sensing. Infrared thermography image analysis was performed on both pristine and damaged T-joint specimens subject to pull-off testing. Experimental results showed that the heat generated by SMA tufts measured by an infrared camera provided accurate indication of delamination perpendicular to the tuft direction. SMA tufts were also used as strain sensors embedded within the T-joint. Local changes of the electrical resistance in SMA filaments, both separately and within the joint, were observed during pulling loads. Digital Image Correlation measurements exhibited good correlation between electrical resistance variations and the opening of delamination. These results pave the way for the development of multifunctional composite joining systems combining enhanced through-the-thickness damage tolerance and self-sensing capabilities.

Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries.

In recent years, infrared thermographic (IRT) technology has experienced notable advancements and found widespread applications in various fields, such as renewable industry, electronic industry, construction, aviation, and healthcare. IRT technology is used for defect detection due to its non-contact, efficient, and high-resolution methods, which enhance product quality and reliability. This review offers an overview of active IRT principles. It comprehensively examines four categories based on the type of heat sources employed: pulsed thermography (PT), lock-in thermography (LT), ultrasonically stimulated vibration thermography (UVT), and eddy current thermography (ECT). Furthermore, the review explores the application of IRT imaging in the renewable energy sector, with a specific focus on the photovoltaic (PV) industry. The integration of IRT imaging and deep learning techniques presents an efficient and highly accurate solution for detecting defects in PV panels, playing a critical role in monitoring and maintaining PV energy systems. In addition, the application of infrared thermal imaging technology in electronic industry is reviewed. In the development and manufacturing of electronic products, IRT imaging is used to assess the performance and thermal characteristics of circuit boards. It aids in detecting potential material and manufacturing defects, ensuring product quality. Furthermore, the research discusses algorithmic detection for PV panels, the excitation sources used in electronic industry inspections, and infrared wavelengths. Finally, the review analyzes the advantages and challenges of IRT imaging concerning excitation sources, the PV industry, the electronics industry, and artificial intelligence (AI). It provides insights into critical issues requiring attention in future research endeavors.

Long-term Changes of Infrared Thermography in Successful Decompression of Carpal Tunnel Syndrome: a Case Report

Abstract Introduction Infrared thermography (IRT) has demonstrated high diagnostic accuracy for carpal tunnel syndrome (CTS) diagnosis in previous studies. However, the recovery of the autonomic function after treatment for CTS is rarely addressed in the literature, especially on the long-term. Case Presentation A 59-year-old lady sought treatment for a long-term history of numbness, tingling, and hand and arm pain. CTS was diagnosed by clinical and electrophysiological means. After 6 months of conservative treatment, surgical treatment was offered. Preoperative IRT was performed by static and dynamic evaluations immediately and 5 minutes after the cold challenge test using the FLIR C2 camera with accuracy of 2°C or 2%. Fingers were consistently colder (mean of 3.76°C), which clearly represented an autonomic dysfunction in the patient's hand. The patient underwent mini-open carpal tunnel decompression and did great postoperatively. One year after surgery, the patient was fully recovered and completely asymptomatic. IRT imaging showed a remarkable improvement of fingers temperature (mean of 3.36°C). Conclusions Our long-term results confirmed that functional recovery occurred concomitantly to autonomic recovery, which was demonstrated by consistent improvement in fingers' temperature. IRT has a strong potential at the evaluation of patients with CTS for both diagnosis and follow-up.

An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images

BackgroundTwo types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtained by ultrasound imaging primarily expresses the size, shape, contour boundary, echo, and other morphological information of the lesion, while the infrared thermal image obtained by infrared thermography imaging primarily describes its thermodynamic function information. Although distinguishing between benign and malignant thyroid nodules requires both morphological and functional information, present deep learning models are only based on US images, making it possible that some malignant nodules with insignificant morphological changes but significant functional changes will go undetected.ResultsGiven the US and IRT images present thyroid nodules through distinct modalities, we proposed an Adaptive multi-modal Hybrid (AmmH) classification model that can leverage the amalgamation of these two image types to achieve superior classification performance. The AmmH approach involves the construction of a hybrid single-modal encoder module for each modal data, which facilitates the extraction of both local and global features by integrating a CNN module and a Transformer module. The extracted features from the two modalities are then weighted adaptively using an adaptive modality-weight generation network and fused using an adaptive cross-modal encoder module. The fused features are subsequently utilized for the classification of thyroid nodules through the use of MLP. On the collected dataset, our AmmH model respectively achieved 97.17% and 97.38% of F1 and F2 scores, which significantly outperformed the single-modal models. The results of four ablation experiments further show the superiority of our proposed method.ConclusionsThe proposed multi-modal model extracts features from various modal images, thereby enhancing the comprehensiveness of thyroid nodules descriptions. The adaptive modality-weight generation network enables adaptive attention to different modalities, facilitating the fusion of features using adaptive weights through the adaptive cross-modal encoder. Consequently, the model has demonstrated promising classification performance, indicating its potential as a non-invasive, radiation-free, and cost-effective screening tool for distinguishing between benign and malignant thyroid nodules. The source code is available at https://github.com/wuliZN2020/AmmH.

A flexible deep learning framework for thermographic inspection of composites

Infrared thermography (IRT) is a promising inspection technique, showing good defect detectability in a range of materials. To advance the IRT inspection technique, automated defect detection and analysis are of high interest. This study proposes an object detection algorithm based on Faster R–CNN with attention-based feature fusion network and flexible late fusion strategy for efficient extraction of defect features from an IRT image sequence. In order to train the deep learning-based algorithm, a large, diverse and representative virtual thermographic dataset for composite materials was constructed by an in-house developed parameterized 3D finite element simulator. The virtually trained deep learning framework is tested on experimental IRT datasets which were obtained on composite coupons having a range of artificial defect types as well as on stiffened aerospace composite panels with real (production) defects. The obtained test results indicate the high performance, generalization and reliability of the proposed deep learning framework for automated thermographic inspection of composite parts.

Infrared thermography monitoring of rock faces – Potential and pitfalls

Rock surface temperatures are of fundamental importance for studies on weathering, rockfall and permafrost. Point temperature measurements may not reflect the small-scale variability of temperature as a function of micro-topography. To close this gap, infrared thermography (IRT) seems to be a simple and promising approach. However, there are several pitfalls in terms of interpretation, as radiation temperatures depend on emissivity and reflectivity of the rock, which in turn are influenced by rock type, surface roughness, wetness, surrounding weather conditions, and angle to the camera axis.We performed laboratory and exemplary field experiments in order to estimate the magnitude of possible errors. We used rough and smooth (sawn) specimen of six different stone types in wet and dry condition and took IRT images at different tilt angles between camera axis and rock surface. Furthermore, we applied the approach to a small rock outcrop (approx. 3 × 3 m) and to a rockwall (approx. 100 × 100 m).The results of the laboratory measurements show that the temperature error increases with increasing tilt angle of the rock surface. Depending on the nature of the reflected surroundings, radiation temperatures can be warmer or cooler than sensor temperatures. In typical settings, the error is low (<0.5 K) up to a tilt of 40° but it may increase to >1 K at tilt angles of 50° and more. Smooth and wet surfaces tend to be more prone to deviations.The field examples confirm the results of the laboratory tests. But they show that spatial differences in temperature can still be detected as the “true” differences are usually larger than the magnitude of error. We suggest to reduce the error of the IRT image by correcting temperatures using a high-resolution surface model.

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments.

Infrared Thermography Imaging for Assessment of Peripheral Perfusion in Patients with Septic Shock.

Skin temperature changes can be used to assess peripheral perfusion in circulatory shock patients. However, research has been limited to point measurements from acral parts of the body. Infrared thermography allows non-invasive evaluation of temperature distribution over a larger surface. Our study aimed to map thermographic patterns in the knee and upper thigh of 81 septic shock patients within 24 h of admission and determine the relationship between skin temperature patterns, mottling, and 28-day mortality. We extracted skin temperature measurements from zones corresponding to mottling scores and used a linear mixed model to analyze the distribution of skin temperature in patients with different mottling scores. Our results showed that the distribution of skin temperature in the anterior thigh and knee is physiologically heterogeneous and has no significant association with mottling or survival at 28 days. However, overall skin temperature of the anterior thigh and knee is significantly lower in non-survivors when modified by mottling score. No differences were found in skin temperature between the survivor and non-survivor groups. Our study shows the potential usefulness of infrared thermography in evaluating skin temperature patterns in resuscitated septic shock patients. Overall skin temperature of the anterior thigh and knee may be an important indicator of survival status when modified by mottling score.

Assessment of Machine and Deep Learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography

Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms.

Intra- and inter-session reliability and repeatability of an infrared thermography device designed for materials to measure skin temperature of the triceps surae muscle tissue of athletes.

Infrared thermography devices have been commonly applied to measure superficial temperature in structural composites and walls. These tools were cheaper than other thermographic devices used to measure superficial human muscle tissue temperature. In addition, infrared thermography has been previously used to assess skin temperature related to muscle tissue conditions in the triceps surae of athletes. Nevertheless, the reliability and repeatability of an infrared thermography device designed for materials, such as the Manual Infrared Camera PCE-TC 30, have yet to be determined to measure skin temperature of the triceps surae muscle tissue of athletes. The purpose was to determine the procedure's intra- and inter-session reliability and repeatability to determine skin temperature within the Manual Infrared Camera PCE-TC 30 thermography device in the triceps surae muscle tissue of athletes, which was initially designed to measure the superficial temperature of materials. A total of 34 triceps surae muscles were bilaterally assessed from 17 healthy athletes using the Manual Infrared Camera PCE-TC 30 thermography device to determine intra- (at the same day separated by 1 h) and inter-session (at alternate days separated by 48 h) reliability and repeatability of the skin temperature of the soleus, medial and lateral gastrocnemius muscles. The triceps surae complex weas measured by a region of interest of 1 cm2 through five infrared thermography images for each muscle. Statistical analyses comprised intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimum detectable change (MCD), systematic error of measurement, correlation (r), and Bland-Altman plots completed with linear regression models (R 2). Intra- and inter-session measurements of the proposed infrared thermography procedure showed excellent reliability (ICC(1,2) = 0.968-0.977), measurement errors (SEM = 0.186-0.232 °C; MDC = 0.515-0.643 °C), correlations (r = 0.885-0.953), and did not present significant systematic error of measurements (P > 0.05). Adequate agreement between each pair of measurement moments was presented by the Bland-Altman plots according to the limits of agreement and non-significant linear regression models (R 2 = 0.000-0.019; P > 0.05). The proposed procedure to determine skin temperature within the Manual Infrared Camera PCE-TC 30 thermography device presented excellent intra- and inter-session reliability and repeatability in athletes' triceps surae muscle tissue. Future studies should consider the SEM and MDC of this procedure to measure the skin temperature of soleus, medial, and lateral gastrocnemius muscles to promote triceps surae muscle prevention and recovery in athletes.

Early Skin Temperature Characteristics of the Kennedy Lesion (Kennedy Terminal Ulcer)

BACKGROUND: Pressure injuries are associated with skin temperature changes, but little is known about skin temperature characteristics of the Kennedy Lesion (KL). PURPOSE: The purpose of this study was to describe early skin temperature changes in KLs using long-wave infrared thermography. METHODS: KLs were identified from chart review in 10 ICU patients. Skin assessments were performed within 24 hours of new skin discoloration. Temperature measurements were performed using a long-wave infrared thermography imaging system. Relative Temperature Differential (RTD) between the discolored area and a selected control point was calculated. RTDs of &gt; +1.2 °C and &lt; -1.2 °C were considered abnormal. Demographic data and observable characteristics of the KL were collected when available. Descriptive statistics (Mean +/- SD; %) were used. RESULTS: The major finding of this study was that there were no early skin temperature differences between the KLs and surrounding skin. CONCLUSION: The early stage of the KL may be limited to microvascular injury which results in a normal skin temperature. More studies are needed to verify this finding and to ascertain whether KL skin temperature changes over time. The study also supports the bedside use of thermography in skin temperature assessment.

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.

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.

Broad-spectrum infrared thermography for detection of M2 digital dermatitis lesions on hind feet of standing dairy cattle.

Low-effort, reliable diagnostics of digital dermatitis (DD) are needed, especially for lesions warranting treatment, regardless of milking system or hygienic condition of the feet. The primary aim of this study was to test the association of infrared thermography (IRT) from unwashed hind feet with painful M2 lesions under farm conditions, with lesion detection as ultimate goal. Secondary objectives were to determine the association between IRT from washed feet and M2 lesions, and between IRT from unwashed and washed feet and the presence of any DD lesion. A total of 641 hind feet were given an M-score and IRT images of the plantar pastern were captured. Multivariable logistic regression analyses were done with DD status as dependent variable and maximum infrared temperature (IRTmax), lower leg cleanliness score and locomotion score as independent variables, and farm as fixed effect. To further our understanding of IRTmax within DD status, we divided IRTmax into two groups over the median value of IRTmax in the datasets of unwashed and washed feet, respectively, and repeated the multivariable logistic regression analyses. Higher IRTmax from unwashed hind feet were associated with M2 lesions or DD lesions, in comparison with feet without an M2 lesion or without DD, adjusted odds ratio 1.6 (95% CI 1.2-2.2) and 1.1 (95% CI 1.1-1.2), respectively. Washing of the feet resulted in similar associations. Dichotomization of IRTmax substantially enlarged the 95% CI for the association with feet with M2 lesions indicating that the association becomes less reliable. This makes it unlikely that IRTmax alone can be used for automated detection of feet with an M2 lesion. However, IRTmax can have a role in identifying feet at-risk for compromised foot health that need further examination, and could therefore function as a tool aiding in the automated monitoring of foot health on dairy herds.

Termografia infravermelha na detecção de mastite clínica e subclínica em bovinos de leite: comparação entre as raças Girolando e Jersey

Abstract Mastitis is one of the most prevalent diseases in dairy cattle globally, ranking at the top in terms of prevalence and incidence. It impacts milk production and quality, subsequently decreasing economic returns and farm sustainability. Early diagnosis and treatment of mastitis are crucial to mitigate its detrimental effects on both animals and the dairy industry. Infrared thermography (IRT) in animals serves as a clinically relevant method to detect pathophysiological changes, marked by thermal variations caused by inflammation. This study aimed to evaluate the potential of IRT as a diagnostic tool for clinical and subclinical mastitis in Girolando and Jersey cows. We examined 78 udder quarters from Girolando cows and 104 from Jersey cows, all from farms in the Adamantina region. Differences in IRT image intensities were compared with anterior and posterior udder temperatures at a single central point or area, correlating with results from Tamis and CMT tests. All analyses were conducted in R software, with a significance level set at 5%. When evaluating thermographic images, the effect size was significant for the breed and CMT test, but not for the Tamis test. In conclusion, IRT exhibits potential in screening for subclinical mastitis in the evaluated breeds, demonstrating a predictive diagnostic capability similar to the CMT, albeit with a temperature difference between them. Their measurements, whether at a point or an area of the mammary gland, were found to be equivalent.

Fault Detection in Electrical Equipment by Infrared Thermography Images Using Spiking Neural Network Through Hybrid Feature Selection

The Spiking Neural Network (SNN) model is a third-generation neural network model that uses spike pulse trains to detect and classify electrical problems in the electrical equipment under consideration. To diagnose the thermal issue in the early stages, it is required to evaluate and monitor the electrical components. The detection method in Infrared Thermography (IRT) is hybrid optimization, which is a Dragonfly Algorithm (DA)–Ant Colony Optimization (ACO) technique that produces a higher exploration and exploitation rate while moving toward an optimal solution with a higher convergence rate. The innovative hybrid DA–ACO method used in this study aims to find the best weights for the SNN model while also extracting the most useful characteristics for defect detection in electrical equipment. To improve classification accuracy, the optimum features picked using the created hybrid DA–ACO are provided to the SNN model. Based on conventional and other optimization methods, the proposed method provided superior results from the execution results.