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Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition

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  • João Antunes Rodrigues

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal
    EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Universidade Lusófona, Campo Grande 376, 1749-024 Lisboa, Portugal)

  • José Torres Farinha

    (Polytechnic of Coimbra—ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
    Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, 3030-290 Coimbra, Portugal)

  • Mateus Mendes

    (Polytechnic of Coimbra—ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
    Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-194 Coimbra, Portugal)

  • Ricardo J. G. Mateus

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

  • António J. Marques Cardoso

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal)

Abstract

Forecasting has extreme importance in industry due to the numerous competitive advantages that it provides, allowing to foresee what might happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset’s behaviour several days in advance.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6308-:d:901088
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    References listed on IDEAS

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    1. 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.
    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. Jonsson, Patrik, 1999. "Company-wide integration of strategic maintenance: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 60(1), pages 155-164, April.
    4. 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.
    5. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
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    Cited by:

    1. Roland Bolboacă & Piroska Haller, 2023. "Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data," Mathematics, MDPI, vol. 11(6), pages 1-35, March.
    2. 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.
    3. João Antunes Rodrigues & Alexandre Martins & Mateus Mendes & José Torres Farinha & Ricardo J. G. Mateus & Antonio J. Marques Cardoso, 2022. "Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.

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