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

João Antunes Rodrigues (), José Torres Farinha, Mateus Mendes (), Ricardo J. G. Mateus and António J. Marques Cardoso
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João Antunes Rodrigues: CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal
José Torres Farinha: Polytechnic of Coimbra—ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
Mateus Mendes: Polytechnic of Coimbra—ISEC, Quinta da Nora, 3030-199 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

Energies, 2022, vol. 15, issue 17, 1-16

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.

Keywords: maintenance; neural networks; XGBoost; forecast; sensor prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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