Early Fault Detection in Paper Machine Motors Using Machine Learning
DOI:
https://doi.org/10.61961/injei.v2i1.18Keywords:
Machine Learning, Predictive Maintenance, Paper Machine, DTC, SimscapeAbstract
This research addresses the application of a neural network as a tool for early fault detection in the motors of a paper machine under a simulated environment. It proposes the analysis of variables from a torque control loop. The data for training and validating the model is obtained through the simulation of Direct Torque Control (DTC) of an AC motor in Simscape within Simulink. Both normal and faulty operating modes are considered. Under these two scenarios, various speed setpoints are configured, and the necessary data for training the developed model is collected
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G. M. M. Fernández Cabañas Manés, Técnicas para el mantenimiento y diagnóstico de máquinas eléctricas rotativas. Gran Vía de les Corts Catalanes, 594, 08007 Barcelona: Marcombo, 1998.
J. L. Zhe Li, Qian He, “A survey of deep learning-driven architecture for predictive maintenance,” Engineering Applications of Artificial Intelligence, vol. 133, 2024.
M. J. Gupta Suraj, Kumar Akhilesh, “A critical review on system architecture, techniques, trends, and challenges in intelligent predictive maintenance,” Safety Science, vol. 177, 2024.
J. Buele, F. A. Chicaiza, M. León, and A. P. Sánchez, “Virtual rehabilitation system for fine motor skills using artificial neural networks,” in IOP Conference Series: Materials Science and Engineering, vol. 1070, no. 1. IOP Publishing, 2021, p. 012054.
E. Slawiñski, F. Rossomando, F. A. Chicaiza, J. Moreno-Valenzuela, and V. Mut, “LSTM network in bilateral teleoperation of a skid-steering robot,” Neurocomputing, p. 128248, 2024.
R. Dagner, “Machine learning para mejorar la gestión de mantenimiento de máquinas industriales,” Universidad César Vallejo, 2021.
Y. U. Muhammed Fatih Pekşen, Ulaş Yurtsever, “Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT,” Alexandria Engineering Journal, vol. 96, pp. 112–123, 2024.
G. P. Mostowski Daniel, Jakubczak Krzysztof, “Automated laser beam characterization using artificial intelligence (AI) for the predictive maintenance of lasers,” Optics and Laser Technology, 2024.
K. R. Santoshi Anusha, “Digital transformation technologies for conveyor belts predictive maintenance: a review,” Indonesian Journal of Electrical Engineering and Computer Science, pp. 639–646, 2024.
Y. D. Tangbin Xia, Yimin Jiang, “Intelligent maintenance framework for reconfigurable manufacturing with deep-learning-based prognostics,” IEEE Internet of Things Journal, vol. 11, 2024.
B. Patrik, “Smart condition monitoring using machine learning,” SPE Middle East Intelligent Oil and Gas Symposium, 2017.
L. R. J. R. C. R. A. Guerrero Cano Manuel, Luque Sendra Amalia, “Predictive maintenance using machine learning techniques,” Alexandria Engineering Journal, vol. 96, pp. 112–123, 2024.
MathWorks®, “Simscape,” la.mathworks.com/products/simscape.html, 2024, accessed on june 2024.
L. S. J. Alfonso, Deep Learning: Teoría y Aplicaciones, Alpha Editorial, vol. 1, pp. 93–95, 2021.
MathWorks®, “Direct torque control of an induction motor drive,” https://la.mathworks.com/help/sps/ug/power-motordrive-IM-DTC-HYST.html, 2024, accessed on june 2024.
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Copyright (c) 2024 Cristian Chuchico, Oscar Acosta Agudelo
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