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

Balduíno Mateus; Mateus Mendes; José Torres Farinha; Alexandre Batista Martins; António Marques Cardoso

doi:10.1007/978-3-030-99075-6_2

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

Balduíno Mateus, Mateus Mendes, José Torres Farinha, Alexandre Batista Martins, António Marques Cardoso

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering
Editors	Hao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
Publisher	Springer Science and Business Media B.V.
Pages	11-25
Number of pages	15
ISBN (Print)	9783030990749
DOIs	https://doi.org/10.1007/978-3-030-99075-6_2
Publication status	Published - 2023
Event	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Tianjin, China Duration: 20 Oct 2021 → 23 Oct 2021

Publication series

Name	Mechanisms and Machine Science
Volume	117
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Country/Territory	China
City	Tianjin
Period	20/10/21 → 23/10/21

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

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.

Funders	Funder number
Horizon 2020 Framework Programme	871284
Fundação para a Ciência e a Tecnologia	UIDB/04131/2020, PTDC/EEI-EEE/29494/2017, UIDP/04131/2020
European Regional Development Fund	POCI-01-0145-FEDER-029494

Keywords

ARIMA
Autoregressive models
Data analysis
Deep Learning
Predictive maintenance
Time series forecasting

Access to Document

10.1007/978-3-030-99075-6_2

Cite this

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 H. Zhang, G. Feng, H. Wang, F. Gu, & J. K. Sinha (Eds.), Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering (pp. 11-25). (Mechanisms and Machine Science; Vol. 117). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-030-99075-6_2

Mateus, Balduíno ; Mendes, Mateus ; Farinha, José Torres et al. / Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry. Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. editor / Hao Zhang ; Guojin Feng ; Hongjun Wang ; Fengshou Gu ; Jyoti K. Sinha. Springer Science and Business Media B.V., 2023. pp. 11-25 (Mechanisms and Machine Science).

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

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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author = "Baldu{\'i}no Mateus and Mateus Mendes and Farinha, {Jos{\'e} Torres} and Martins, {Alexandre Batista} and Cardoso, {Ant{\'o}nio Marques}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 ; Conference date: 20-10-2021 Through 23-10-2021",

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Mateus, B, Mendes, M, Farinha, JT, Martins, AB & Cardoso, AM 2023, Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry. in H Zhang, G Feng, H Wang, F Gu & JK Sinha (eds), Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. Mechanisms and Machine Science, vol. 117, Springer Science and Business Media B.V., pp. 11-25, 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, Tianjin, China, 20/10/21. https://doi.org/10.1007/978-3-030-99075-6_2

Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry. / Mateus, Balduíno; Mendes, Mateus; Farinha, José Torres et al.
Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. ed. / Hao Zhang; Guojin Feng; Hongjun Wang; Fengshou Gu; Jyoti K. Sinha. Springer Science and Business Media B.V., 2023. p. 11-25 (Mechanisms and Machine Science; Vol. 117).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry

AU - Mateus, Balduíno

AU - Mendes, Mateus

AU - Farinha, José Torres

AU - Martins, Alexandre Batista

AU - Cardoso, António Marques

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - ARIMA

KW - Autoregressive models

KW - Data analysis

KW - Deep Learning

KW - Predictive maintenance

KW - Time series forecasting

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U2 - 10.1007/978-3-030-99075-6_2

DO - 10.1007/978-3-030-99075-6_2

M3 - Conference contribution

AN - SCOPUS:85138830718

SN - 9783030990749

T3 - Mechanisms and Machine Science

SP - 11

EP - 25

BT - Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering

A2 - Zhang, Hao

A2 - Feng, Guojin

A2 - Wang, Hongjun

A2 - Gu, Fengshou

A2 - Sinha, Jyoti K.

PB - Springer Science and Business Media B.V.

T2 - 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021

Y2 - 20 October 2021 through 23 October 2021

ER -

Mateus B, Mendes M, Farinha JT, Martins AB, Cardoso AM. 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 JK, editors, Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. Springer Science and Business Media B.V. 2023. p. 11-25. (Mechanisms and Machine Science). doi: 10.1007/978-3-030-99075-6_2

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

Abstract

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Bibliographical note

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Keywords

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