Abstract
The economic sustainability of any industry is directly linked to the management and efficiency of its physical assets. The maintenance of these assets is one of the key elements for the success of a company since it represents a relevant part of its Capital and Operational Expenses (CAPEX and OPEX). Due to the importance of maintenance, a lot of research has been done to improve the methodologies aiming to maximize physical assets’ availability at the most rational costs. The introduction of Artificial Intelligence in the world of maintenance increased the quality of prediction on equipment failures, namely when associated to continuous equipment monitoring. This paper presents a case study where a neural network is proposed to predict the future values of various sensors installed on a paper pulp press. Data from the following variables is processed: electric current; pressure; temperature; torque; and speed.
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Acknowledgements
This research is sponsored by FEDER funds through the program COMPETE—Programa Operacional Factores de Competitividade—and by national funds through FCT—Fundação para a Ciência e a Tecnologia—under the project UIDB/00285/2020. This work was produced with the support of INCD funded by FCT and FEDER under the project 01/SAICT/2016 no 022153.
Conflicts of Interest
The authors declare no conflict of interest.
Funding
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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Rodrigues, J.A., Farinha, J.T., Cardoso, A.M., Mendes, M., Mateus, R. (2023). Prediction of Sensor Values in Paper Pulp Industry Using Neural Networks. 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_24
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DOI: https://doi.org/10.1007/978-3-030-99075-6_24
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