Feature extraction and classification for detecting the thermal faults in electrical installations

Jadin, Mohd Shawal; Taib, Soib; Ghazali, Kamarul Hawari

doi:10.1016/j.measurement.2014.07.010

Cited by 40 publications

(13 citation statements)

References 16 publications

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“…To obtain the thermal matrix, Equation (1) proposed by Jadin et al [ 46 ] was used:

where

is the thermal value of the pixel of the thermogram in question;

and

are the values of the minimum and maximum temperature in °C, respectively;

represents the gray scale value of the pixel;

is the largest gray scale value within the thermogram.…”

Section: Methodsmentioning

confidence: 99%

Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care

Jaramillo-Quintanar

Cruz-Albarran

Guzman-Sandoval

et al. 2020

Sensors

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Children from out-of-home care are a vulnerable population that faces high stress and anxiety levels due to stressful experiences, such as being abused, being raped, and violence. This problem could have negative effects on their bio-psycho-social well-being if they are not provided with comprehensive psychological treatment. Numerous methods have been developed to help them relax, but there are no current approaches for assessing the relaxation level they reach. Based on this, a novel smart sensor that can evaluate the level of relaxation a child experiences is developed in this paper. It evaluates changes in thermal biomarkers (forehead, right and left cheek, chin, and maxillary) and heart rate (HR). Then, through a k-nearest neighbors (K-NN) intelligent classifier, four possible levels of relaxation can be obtained: no-relax, low-relax, relax, and very-relax. Additionally, an application (called i-CARE) for anxiety management, which is based on biofeedback diaphragmatic breathing, guided imagery, and video games, is evaluated. After testing the developed smart sensor, an 89.7% accuracy is obtained. The smart sensor used provides a reliable measurement of relaxation levels and the i-CARE application is effective for anxiety management, both of which are focused on children exposed to out-of-home care conditions.

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“…To obtain the thermal matrix, Equation (1) proposed by Jadin et al [ 46 ] was used:

where

is the thermal value of the pixel of the thermogram in question;

and

are the values of the minimum and maximum temperature in °C, respectively;

represents the gray scale value of the pixel;

is the largest gray scale value within the thermogram.…”

Section: Methodsmentioning

confidence: 99%

Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care

Jaramillo-Quintanar

Cruz-Albarran

Guzman-Sandoval

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The threshold processing technique has been widely used in various image segmentation tasks due to its simplicity and ease of implementation [11]. When studying the thermal state of electrical equipment, the threshold treatment method is a very useful tool for separating a warm region from its background [12]. Possible thermal anomalies in equipment can be detected by filtering the image using a certain threshold value [13].…”

Section: Automatic Condition Monitoring Methods To Find Defects Of a Hmentioning

confidence: 99%

Automatic condition monitoring method to find defects in high-voltage insulators using infrared images

2019

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In recent years, infrared imaging has become an important tool, particularly for predicting and preventing electrical equipment failure. Systems for online monitoring of the equipment conditions used in electrical substations are based on computer vision algorithms to perform visual analysis, automatically detect and assess equipment condition. This article describes a developed method that automatically finds defects in high-voltage insulators using infrared images. This method is based on the Otsu method, which is one of the most popular and effective segmentation methods that can be applied to finding defects in infrared images. The result is a comparative analysis of computer vision methods in infrared images used in our research. Automatic condition monitoring to find defects in high-voltage insulators in infrared images can be considered as the base method for an automated thermal imaging system for monitoring electrical substation equipment.

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“…La base de datos utilizada en este trabajo corresponde a 815 imágenes térmicas, pertenecientes a una empresa de distribución eléctrica peruana, clasificadas en dos categorías, subestaciones eléctricas y líneas de transmisión. Este conjunto de datos es superior en tamaño al utilizado en [18], [20], [22], [36] y [37]. Las imágenes fueron capturadas mediante la cámara infrarroja TP8S, cuyas especificaciones se aprecian en la Tabla 1.…”

Section: Base De Datosunclassified

Transfer learning en la clasificación binaria de imágenes térmicas

2021

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La clasificación de imágenes térmicas es un aspecto clave en el sector industrial, debido a que suele ser el punto de partida en la detección de fallos en equipos eléctricos. En algunos casos, esta tarea se automatiza mediante el uso de técnicas tradicionales de inteligencia artificial, mientras que en otros, es realizada de manera manual, lo cual puede traer consigo altas tasas de error humano. Este artículo presenta un análisis comparativo entre once arquitecturas de transfer learning (AlexNet, VGG16, VGG19, ResNet, DenseNet, MobileNet v2, GoogLeNet, ResNeXt, Wide ResNet, MNASNet y ShuffleNet) mediante el uso de fine-tuning, con la finalidad de realizar una clasificación binaria de imágenes térmicas en una red de distribución eléctrica. Para ello, se dispone de una base de datos con 815 imágenes, divididas mediante la técnica tipo hold-out 60-20-20 y validación cruzada con 5-folds, para finalmente analizar su rendimiento mediante el test de Friedman. Luego de los experimentos, se obtuvieron resultados satisfactorios con exactitudes superiores a 85 % en diez de las arquitecturas previamente entrenadas. Sin embargo, la arquitectura que no se entrenó previamente presentó una exactitud baja; concluyéndose que la aplicación de transfer learning mediante el uso de arquitecturas previamente entrenadas es un mecanismo adecuado en la clasificación de este tipo de imágenes, y representa una alternativa confiable frente a técnicas tradicionales de inteligencia artificial.

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Feature extraction and classification for detecting the thermal faults in electrical installations

Cited by 40 publications

References 16 publications

Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care

Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care

Automatic condition monitoring method to find defects in high-voltage insulators using infrared images

Transfer learning en la clasificación binaria de imágenes térmicas

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