Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gat...

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Autores principales:	Balduíno César Mateus, Mateus Mendes, José Torres Farinha, Rui Assis, António Marques Cardoso
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	LSTM recurrent neural network GRU paper press predictive maintenance Technology T
Acceso en línea:	https://doaj.org/article/7de9e1b39c8f490c82767493d3dc258e
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spelling	oai:doaj.org-article:7de9e1b39c8f490c82767493d3dc258e2021-11-11T15:46:56ZComparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press10.3390/en142169581996-1073https://doaj.org/article/7de9e1b39c8f490c82767493d3dc258e2021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6958https://doaj.org/toc/1996-1073The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.Balduíno César MateusMateus MendesJosé Torres FarinhaRui AssisAntónio Marques CardosoMDPI AGarticleLSTMrecurrent neural networkGRUpaper presspredictive maintenanceTechnologyTENEnergies, Vol 14, Iss 6958, p 6958 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	LSTM recurrent neural network GRU paper press predictive maintenance Technology T
spellingShingle	LSTM recurrent neural network GRU paper press predictive maintenance Technology T Balduíno César Mateus Mateus Mendes José Torres Farinha Rui Assis António Marques Cardoso Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
description	The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.
format	article
author	Balduíno César Mateus Mateus Mendes José Torres Farinha Rui Assis António Marques Cardoso
author_facet	Balduíno César Mateus Mateus Mendes José Torres Farinha Rui Assis António Marques Cardoso
author_sort	Balduíno César Mateus
title	Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
title_short	Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
title_full	Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
title_fullStr	Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
title_full_unstemmed	Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
title_sort	comparing lstm and gru models to predict the condition of a pulp paper press
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7de9e1b39c8f490c82767493d3dc258e
work_keys_str_mv	AT balduinocesarmateus comparinglstmandgrumodelstopredicttheconditionofapulppaperpress AT mateusmendes comparinglstmandgrumodelstopredicttheconditionofapulppaperpress AT josetorresfarinha comparinglstmandgrumodelstopredicttheconditionofapulppaperpress AT ruiassis comparinglstmandgrumodelstopredicttheconditionofapulppaperpress AT antoniomarquescardoso comparinglstmandgrumodelstopredicttheconditionofapulppaperpress
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Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

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