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Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry

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Proceedings of IncoME-VI and TEPEN 2021

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 117))

Abstract

Predictive maintenance is fundamental for modern industries, in order to improve the physical assets availability, decision making and rationalize costs. That requires deployment of sensor networks, data storage and development of data treatment methods that can satisfy the quality required in the forecasting models. The present paper describes a case study where data collected in an industrial pulp paper press was pre-processed and used to predict future behavior, aiming to anticipate potential failures, optimize predictive maintenance and physical assets availability. The data were processed and analyzed, outliers identified and treated. Time series models were used to predict short-term future behavior. The results show that it is possible to predict future values up to ten days in advance with good accuracy.

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Acknowledgements

The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowvska-Curie grant agreement 871284 project SSHARE and the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-029494, and by National Funds through the FCT—Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/29494/2017, UIDB/04131/2020, and UIDP/04131/2020.

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Authors and Affiliations

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

    Balduíno Mateus & Alexandre Batista Martins

  2. CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte Do Lameiro, P–62001-001, Covilhã, Portugal

    Balduíno Mateus, Alexandre Batista Martins & António Marques Cardoso

  3. CEMMPRE—Centre for Mechanical Engineering, Materials and Processes—CEMMPRE, 3030-788, Coimbra, Portugal

    José Torres Farinha

  4. Polytechnic of Coimbra—Polytechnic of Coimbra, ISEC, 3045-093, Coimbra, Portugal

    Mateus Mendes & José Torres Farinha

  5. Institute of Systems and Robotics, University of Coimbra, 3004-531, Coimbra, Portugal

    Mateus Mendes

Authors
  1. Balduíno Mateus
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  2. Mateus Mendes
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  3. José Torres Farinha
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  4. Alexandre Batista Martins
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  5. António Marques Cardoso
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Corresponding author

Correspondence to Balduíno Mateus .

Editor information

Editors and Affiliations

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin, China

    Hao Zhang

  2. School of Mechanical Engineering, Hebei University of Technology, Tianjin, China

    Guojin Feng

  3. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China

    Hongjun Wang

  4. University of Huddersfield, Huddersfield, UK

    Fengshou Gu

  5. University of Manchester, Manchester, UK

    Jyoti K. Sinha

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Mateus, B., Mendes, M., Farinha, J.T., Martins, A.B., Cardoso, A.M. (2023). Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry. In: Zhang, H., Feng, G., Wang, H., Gu, F., Sinha, J.K. (eds) Proceedings of IncoME-VI and TEPEN 2021. Mechanisms and Machine Science, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-99075-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99074-9

  • Online ISBN: 978-3-030-99075-6

  • eBook Packages: EngineeringEngineering (R0)

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