Aplicación de modelos de aprendizaje supervisados para la prevención sobre fallos de maquinaria
DOI:
https://doi.org/10.18050.revucvhacer.v12n2a1Keywords:
Mantenimiento predictivo, Aprendizaje automático, Aprendizaje supervisadoAbstract
This article deals with the early detection of machinery failures in Operations Management. The utility of fault detection is discussed as how machine learning, an artificial intelligence technique, can be used to analyze machine monitoring data and detect patterns and signals that indicate a possible failure in the future. The objective of the research is to validate the Machine Learning models for the prediction of failures in machinery. Some of the most common machine learning algorithms are described, such as Support Vector Machine (SVM), Random Forest, CatBoost, and XGBoost. A comparison of metrics is made in the models to review which of them can better predict the detection of a failure in machines.
Keywords: predictive maintenance, machine learning, supervised learning
References
Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Cambridge, MA: MIT Press.
Borja Robalino, R., Monleón Getino, A., & Rodellar, J. (2020). Estandarización de métricas de rendimiento para clasificadores Machine y Deep Learning. Revista Ibérica de Sistemas e Tecnologias de Informação, 30, 184-196.
Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. http://dx.doi.org/10.1023/A:1010933404324
Centro de Aprendizaje Automático y Sistemas Inteligentes (2020). Machine Learning Repository. https://archive.ics.uci.edu/ml/index.php
Chen, T. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. https://doi.org/10.1145/2939672.2939785
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. (19), 8211. MDPI AG. http://dx.doi.org/10.3390/su12198211
Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc.
Huddleston, S. H., & Brown, G. G. (2018). Machine learning. In Informs Analytics Body of Knowledge (1st ed., Vol. 38). https://doi.org/10.1002/9781119505914.ch7
IBM. (2021). Support Vector Machines (SVM). IBM Developer. https://developer.ibm.com/technologies/machine-learning/tutorials/support-vector-machinessvm
López Camuñas, J. M. (2021). Predictive maintenance using deep learning (Bachelor’s thesis). Universitat de Barcelona. https://hdl.handle.net/2445/182392
Matzka, S. (2020). Explainable Artificial Intelligence for Predictive Maintenance Applications. In 2020 Third International Conference on Artificial Intelligence for Industries (AI4I) (pp. 69-74). IEEE. https://doi.org/10.1109/AI4I49448.2020.00023
Prokhorenkova, L., Gusev, G., Vorobev, A., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. https://arxiv.org/abs/1706.09516
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & - Beghi, A. (2015). Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach. IEEE Transactions on Industrial Informatics, 11(3), 812-820. https://ieeexplore.ieee.org/document/6879441
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Copyright (c) 2023 Angel Jian Pan Celestino, Kevin Raul Guillen Bravo, Jorge Luis Roca Becerra
This work is licensed under a Creative Commons Attribution 4.0 International License.
