Prediction of Sensor Values in Paper Pulp Industry Using Neural Networks

João Antunes Rodrigues, José Torres Farinha, António Marques Cardoso, Mateus Mendes, Ricardo Mateus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering
EditorsHao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
PublisherSpringer Science and Business Media B.V.
Pages281-291
Number of pages11
ISBN (Print)9783030990749
DOIs
Publication statusPublished - 2023
Event6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Tianjin, China
Duration: 20 Oct 202123 Oct 2021

Publication series

NameMechanisms and Machine Science
Volume117
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Country/TerritoryChina
CityTianjin
Period20/10/2123/10/21

Bibliographical note

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

Keywords

  • Artificial intelligence
  • Condition monitoring
  • Forecasting
  • Neural networks
  • Predictive maintenance

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