BAYESIAN REGULARIZERS OF ARTIFICIAL NEURAL NETWORKS APPLIED TO THE RELIABILITY FORECAST OF INTERNAL COMBUSTION MACHINES IN THE SHORT-TERM
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Tipo do ITEM
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Artigo Ciêntifico
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Título do Artigo
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BAYESIAN REGULARIZERS OF ARTIFICIAL NEURAL NETWORKS APPLIED TO THE RELIABILITY FORECAST OF INTERNAL COMBUSTION MACHINES IN THE SHORT-TERM
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Descrição
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Predictive as well as preventive maintenance are tools of maintenance programs that aim to increase or
maintain the life expectancy of an equipment through computational techniques and tools. Bearing in
mind that the power generation industry has a high maintenance rate with machines and / or electric
generators stopped, this research aims to develop a computational model for predicting the Reliability Key
Performance Indicator (KPI) to identify how available the equipment will be in a time span of 22 days, for
this the methodology to be used will be based on analyzes and tests of artificial neural network (ANN)
architectures using the Bayesian Regularizers training algorithm, alternating the transfer functions in the
layers hidden to find the best state of convergence and the minimum Root Mean Square Error (RMSE)
value calculated between the real and simulated outputs. According to the results obtained by the training,
validation and test steps, the algorithm presented a RMSE rate of 0.0000104202 and a 99.9% correlation
between the real and simulated values, thus the model is able to identify which machine will have the
greatest efficiency and less efficiency within the defined time span.
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Abstract
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Predictive as well as preventive maintenance are tools of maintenance programs that aim to increase or
maintain the life expectancy of an equipment through computational techniques and tools. Bearing in
mind that the power generation industry has a high maintenance rate with machines and / or electric
generators stopped, this research aims to develop a computational model for predicting the Reliability Key
Performance Indicator (KPI) to identify how available the equipment will be in a time span of 22 days, for
this the methodology to be used will be based on analyzes and tests of artificial neural network (ANN)
architectures using the Bayesian Regularizers training algorithm, alternating the transfer functions in the
layers hidden to find the best state of convergence and the minimum Root Mean Square Error (RMSE)
value calculated between the real and simulated outputs. According to the results obtained by the training,
validation and test steps, the algorithm presented a RMSE rate of 0.0000104202 and a 99.9% correlation
between the real and simulated values, thus the model is able to identify which machine will have the
greatest efficiency and less efficiency within the defined time span.
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Língua do arquivo
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inglês
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Data da Publicação
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Ano 2021
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Palavra-chave
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Reliability
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RNA
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Bayesian Regularizers
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UTE
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Autores
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Ítalo Rodrigo Soares Silva
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Manoel Henrique Reis Nascimento
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Milton Fonseca Júnior
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Ricardo Silva Parente
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Paulo Oliveira Siqueira Júnior
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Jandecy Cabral Leite
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Local
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ITEGAM - Manaus, 2021
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Áreas de Conhecimento
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Otimização de Processos Industriais
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Turma
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Turma 1
