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

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  • Balduíno César Mateus

    (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 Lameiro, 62001-001 Covilhã, Portugal)

  • Mateus Mendes

    (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)

  • José Torres Farinha

    (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)

  • Rui Assis

    (EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal)

  • António Marques Cardoso

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal)

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.

Suggested Citation

  • Balduíno César Mateus & Mateus Mendes & José Torres Farinha & Rui Assis & António Marques Cardoso, 2021. "Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press," Energies, MDPI, vol. 14(21), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6958-:d:662687
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    References listed on IDEAS

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    5. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.
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    7. João Antunes Rodrigues & José Torres Farinha & Mateus Mendes & Ricardo J. G. Mateus & António J. Marques Cardoso, 2022. "Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition," Energies, MDPI, vol. 15(17), pages 1-16, August.

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