IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p6958-d662687.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/6958/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/6958/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Jin-Ting & Xiao, Shengning, 2012. "A note on the modified two-way MANOVA tests," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 519-527.
    2. Arpita Samanta Santra & Jun-Lin Lin, 2019. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(11), pages 1-11, May.
    3. Thomas A Lasko & Joshua C Denny & Mia A Levy, 2013. "Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-13, June.
    4. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Carnero, MaCarmen, 2006. "An evaluation system of the setting up of predictive maintenance programmes," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 945-963.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nurkamilya Daurenbayeva & Almas Nurlanuly & Lyazzat Atymtayeva & Mateus Mendes, 2023. "Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems," Energies, MDPI, vol. 16(8), pages 1-21, April.
    2. Héritier Nsenge Mpia & Simon Nyaga Mwendia & Lucy Waruguru Mburu, 2022. "Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    3. Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    4. 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.
    5. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    3. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    4. Xu, Li-Wen, 2015. "Parametric bootstrap approaches for two-way MANOVA with unequal cell sizes and unequal cell covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 291-303.
    5. Ferreira, Rodrigo J.P. & de Almeida, Adiel Teixeira & Cavalcante, Cristiano A.V., 2009. "A multi-criteria decision model to determine inspection intervals of condition monitoring based on delay time analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(5), pages 905-912.
    6. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    7. Fei Chen & Zhiyang Wang & Yu He, 2023. "A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids," Energies, MDPI, vol. 16(22), pages 1-17, November.
    8. Abrahamsen, Eirik Bjorheim & Milazzo, Maria Francesca & Selvik, Jon T. & Asche, Frank & Abrahamsen, HÃ¥kon Bjorheim, 2020. "Prioritising investments in safety measures in the chemical industry by using the Analytic Hierarchy Process," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    9. Wenchao Ban & Liangduo Shen, 2022. "PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    10. Abbasimehr, Hossein & Paki, Reza, 2021. "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    11. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Anna Kulakowska & Anna Zylka & Karolina Grabowska & Katarzyna Ciesielska & Wojciech Nowak, 2020. "Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)," Energies, MDPI, vol. 13(24), pages 1-16, December.
    12. Yih-Der Lee & Jheng-Lun Jiang & Yuan-Hsiang Ho & Wei-Chen Lin & Hsin-Ching Chih & Wei-Tzer Huang, 2020. "Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data," Energies, MDPI, vol. 13(7), pages 1-20, April.
    13. Amin Eshkiti & Fatemeh Sabouhi & Ali Bozorgi-Amiri, 2023. "A data-driven optimization model to response to COVID-19 pandemic: a case study," Annals of Operations Research, Springer, vol. 328(1), pages 337-386, September.
    14. Traore, M. & Chammas, A. & Duviella, E., 2015. "Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 120-131.
    15. Dana-Mihaela Petroșanu & Alexandru Pîrjan, 2020. "Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network," Sustainability, MDPI, vol. 13(1), pages 1-31, December.
    16. Andrew Bate & Steve F. Hobbiger, 2021. "Artificial Intelligence, Real-World Automation and the Safety of Medicines," Drug Safety, Springer, vol. 44(2), pages 125-132, February.
    17. Ahed Abugabah & Farah Shahid, 2023. "Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    18. Yong Wang & Hui Wang & Xiaoqiang Guo & Xinhua Liu & Xiaowen Liu, 2022. "State Prediction Method for A-Class Insulation Board Production Line Based on Transfer Learning," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    19. Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    20. Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6958-:d:662687. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.