Parâmetros Relevantes Na Análise Termográfica De Um Motor de Indução Trifásico

Moreira, João Pedro Carvalho; Lamim, Paulo; Araújo, Ana Carolina Santos; Moreira, Josemar de Souza; Oliveira, Júlia Ferreira de

doi:10.17648/sbai-2019-111206

Cited by 1 publication

(1 citation statement)

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“…The energy (E) emitted by a body is related to its temperature (T) through the Stefan-Boltzmann equation (Eq. 1), where ε and σ refer to the emissivity and the Stefan-Boltzmann constant (5.6704×10 −8 W/m 2 •K), respectively [4,24].…”

Section: Infrared Thermographymentioning

confidence: 99%

Application of Deep Learning Techniques in the Development of Predictive Maintenance and Fault Detection in Electric Motors

Rezende

Pereira

Barella

et al. 2023

ACRI

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During the last two decades, there has been remarkable growth in the processing capacity of computers and the evolution of digital cameras. As a result, the thermographic technique and thermal analysis became more applied in electromechanical maintenance due to the low measuring device cost. Simultaneously, new methods based on Deep Learning focused on image and video processing have emerged. In this sense, this contribution aims to verify the applicability of using the deep learning technique of convolutional neural networks to classify patterns of thermographic images of a bench grinder. The methodology used was the collection of thermographic pictures of a bench grinder after starting, without, and after applying loads to the discs. This procedure induced a temperature increase in the grinding machine housing since some types of faults in electric motors can be diagnosed due to over-temperature by thermographic inspection. Furthermore, a Python computational code was developed using a convolutional neural network to classify different grinder operation profiles based on thermal images. In conclusion, the technique proved promising for diagnosing motor failures by thermography and can be implemented in automatic predictive maintenance routines.

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Section: Infrared Thermographymentioning

confidence: 99%