TY - GEN
T1 - Prediction of Sensor Values in Paper Pulp Industry Using Neural Networks
AU - Rodrigues, João Antunes
AU - Farinha, José Torres
AU - Cardoso, António Marques
AU - Mendes, Mateus
AU - Mateus, Ricardo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Condition monitoring
KW - Forecasting
KW - Neural networks
KW - Predictive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85138811212&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-99075-6_24
DO - 10.1007/978-3-030-99075-6_24
M3 - Conference contribution
AN - SCOPUS:85138811212
SN - 9783030990749
T3 - Mechanisms and Machine Science
SP - 281
EP - 291
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 -