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

Authors :
Antonio J. Marques Cardoso
Rui Assis
Balduíno César Mateus
Mateus Mendes
José Torres Farinha
Source :
Energies; Volume 14; Issue 21; Pages: 6958, Energies, Vol 14, Iss 6958, p 6958 (2021)
Publication Year :
2021
Publisher :
Multidisciplinary Digital Publishing Institute, 2021.

Abstract

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.

Details

Language :
English
ISSN :
19961073
Database :
OpenAIRE
Journal :
Energies; Volume 14; Issue 21; Pages: 6958
Accession number :
edsair.doi.dedup.....3fd9ccf9fd31b5c6aa7d4861ad66be02
Full Text :
https://doi.org/10.3390/en14216958