Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition
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References listed on IDEAS
- Jonsson, Patrik, 1999. "Company-wide integration of strategic maintenance: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 60(1), pages 155-164, April.
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- Balduíno César Mateus & Mateus Mendes & José Torres Farinha & Rui Assis & António Marques Cardoso, 2021. "Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press," Energies, MDPI, vol. 14(21), pages 1-21, October.
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Cited by:
- Roland Bolboacă & Piroska Haller, 2023. "Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data," Mathematics, MDPI, vol. 11(6), pages 1-35, March.
- Nurkamilya Daurenbayeva & Almas Nurlanuly & Lyazzat Atymtayeva & Mateus Mendes, 2023. "Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems," Energies, MDPI, vol. 16(8), pages 1-21, April.
- João Antunes Rodrigues & Alexandre Martins & Mateus Mendes & José Torres Farinha & Ricardo J. G. Mateus & Antonio J. Marques Cardoso, 2022. "Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.
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Keywords
maintenance; neural networks; XGBoost; forecast; sensor prediction;All these keywords.
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