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

Balduíno César Mateus; Mateus Mendes; José Torres Farinha; Rui Assis; António Marques Cardoso

LAPSE:2023.17946

Published Article

LAPSE:2023.17946

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

Balduíno César Mateus, Mateus Mendes, José Torres Farinha, Rui Assis, António Marques Cardoso

March 7, 2023

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.

Record ID

LAPSE:2023.17946

Keywords

GRU, LSTM, paper press, predictive maintenance, recurrent neural network

Subject

Numerical Methods and Statistics

Suggested Citation

Mateus BC, Mendes M, Farinha JT, Assis R, Cardoso AM. Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press. (2023). LAPSE:2023.17946

Author Affiliations

Mateus BC: EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal; CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameir [ORCID]
Mendes M: Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal; Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal [ORCID]
Farinha JT: Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal; Centre for Mechanical Engineering, Materials and Processes—CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal [ORCID]
Assis R: EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal [ORCID]
Cardoso AM: CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal [ORCID]

Journal Name

Energies

Volume

14

Issue

21

First Page

6958

Year

2021

Publication Date

2021-10-22