Aprendizaje de máquina para mantenimiento predictivo: un problema de clasificación binaria
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Publicado:
May 11, 2022
Palabras clave:
Aprendizaje de máquina, mantenimiento predictivo, detección de fallos, clasificación binaria.
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Resumen
Introducción. Con el auge de la industria 4.0, se están extrayendo de las máquinas y procesos una gran cantidad de datos, los cuales pueden ser analizados mediante enfoques de aprendizaje de máquina, permitiendo una toma de decisiones más confiable dentro del área de mantenimiento; realizar análisis de datos de mantenimiento predictivo se vuelve un verdadero reto para un ser humano debido a la gran cantidad de datos. Objetivo. Por esta razón en el presente estudio, se plantea como objetivo crear un modelo predictivo de aprendizaje de máquina para detectar fallos. Metodología. Para la creación del modelo se utilizó los datos de mantenimiento predictivo ai4i2020 disponibles en el repositorio de Machine Learning de la Universidad de California y el software libre Python. Se probó 4 algoritmos de clasificación, con la finalidad de compararlos en función de las métricas de rendimiento. Resultados. Dando como resultado que SVM es el mejor algoritmo con una exactitud del 98,95% y una precisión de 98,88% (optimizados los hiperparámetros). Conclusiones. Se concluye que el modelo funciona con un elevado rendimiento y una buena generalización de los patrones aprendidos durante el entrenamiento, en datos de prueba o datos no vistos por el algoritmo.
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Vilema Lara, P. H., García Mora, F. A., & Gallegos Londoño, C. M. (2022). Aprendizaje de máquina para mantenimiento predictivo: un problema de clasificación binaria . ConcienciaDigital, 5(2.1), 45-68. https://doi.org/10.33262/concienciadigital.v5i2.1.2150
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Breiman, L. (2001). Random Forests. Machine Learning.
Canizo, M., Onieva, E., Conde, A., Charramendieta, S., & Trujillo, S. (2017). Real-time predictive maintenance for wind turbines using Big Data frameworks. 2017 IEEE International Conference on Prognostics and Health Management, ICPHM 2017, 70-77.
Carvalho, T.P., Soares, F., Vita, R., Francisco, R., Basto, J.P., & Alcalá, S.G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers and Industrial Engineering, 137, 106024.
Dey, A. (2016). Machine Learning Algorithms: A Review. International Journal of Computer Science and Information Technologies, 7 (3), 1174-1179.
Dong, W., Huang, Y., Lehane, B., & Ma, G. (2020). XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, 103155.
Dua, D., & Graff, C. (2019). UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/AI4I+2020+Predictive+Maintenance+Dataset
Fernandes, M., Canito, A., Corchado, J.M., & Marreiros, G. (2020). Fault Detection Mechanism of a Predictive Maintenance System Based on Autoregressive Integrated Moving Average Models. Distributed Computing and Artificial Intelligence, 16th International Conference. DCAI 2019. Advances in Intelligent Systems and Computing, 1003, 171-180.
Gianey, H.K, & Choudhary, R. (2018). Comprehensive Review on Supervised Machine Learning Algorithms. Proceedings - 2017 International Conference on Machine Learning and Data Science, MLDS 2017, 37-43.
Gohel, H.A., Upadhyay, H., Lagos, L., Cooper, K., & Sanzetenea, A. (2020). Predictive maintenance architecture development for nuclear infrastructure using machine learning. Nuclear Engineering and Technology, 52 (7), 1436-1442.
Jijo, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2 (1), 20-28.
Kanawaday, S., & Sane, A. (2018). Machine learning for predictive maintenance of industrial machines using IoT sensor data. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, 87-90.
Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., & Noh, S.D. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing Green Technology, 3 (1), 111-128.
Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M., & Sutherland, J.W. (2019). Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP, 80, 506-511.
Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random Forests. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 743 LNCS, 246-252.
Mitchell, R., & Frank, E. (2017). Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science 3: e127.
Mohammed, R., Rawashdeh, J., & Abdullah, M. (2020). Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results. 2020 11th International Conference on Information and Communication Systems, ICICS 2020, 243-248.
Schwendemann, S., Amjad, Z., & Sikora, A. (2021). A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines. Computers in Industry, 125, 103380.
Uddin, S., Khan, A., Hossain, M.E., & Moni, M.L. (2019). Comparing different supervised machine learning algorithms for disease Prediction. BMC Medical Informatics and Decision Making, 19 (1), 1-16.
Vieira, S., Lopez Pinaya, W.H., & Mechelli, A. (2019). Main concepts in machine learning. Machine Learning: Methods and Applications to Brain Disorders, 21-44.
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A Manufacturing Big Data Solution for Active Preventive Maintenance. IEEE Transactions on Industrial Informatics, 13 (4), 2039-2047.
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production and Manufacturing Research, 4 (1), 23-45.
Breiman, L. (2001). Random Forests. Machine Learning.
Canizo, M., Onieva, E., Conde, A., Charramendieta, S., & Trujillo, S. (2017). Real-time predictive maintenance for wind turbines using Big Data frameworks. 2017 IEEE International Conference on Prognostics and Health Management, ICPHM 2017, 70-77.
Carvalho, T.P., Soares, F., Vita, R., Francisco, R., Basto, J.P., & Alcalá, S.G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers and Industrial Engineering, 137, 106024.
Dey, A. (2016). Machine Learning Algorithms: A Review. International Journal of Computer Science and Information Technologies, 7 (3), 1174-1179.
Dong, W., Huang, Y., Lehane, B., & Ma, G. (2020). XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, 103155.
Dua, D., & Graff, C. (2019). UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/AI4I+2020+Predictive+Maintenance+Dataset
Fernandes, M., Canito, A., Corchado, J.M., & Marreiros, G. (2020). Fault Detection Mechanism of a Predictive Maintenance System Based on Autoregressive Integrated Moving Average Models. Distributed Computing and Artificial Intelligence, 16th International Conference. DCAI 2019. Advances in Intelligent Systems and Computing, 1003, 171-180.
Gianey, H.K, & Choudhary, R. (2018). Comprehensive Review on Supervised Machine Learning Algorithms. Proceedings - 2017 International Conference on Machine Learning and Data Science, MLDS 2017, 37-43.
Gohel, H.A., Upadhyay, H., Lagos, L., Cooper, K., & Sanzetenea, A. (2020). Predictive maintenance architecture development for nuclear infrastructure using machine learning. Nuclear Engineering and Technology, 52 (7), 1436-1442.
Jijo, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2 (1), 20-28.
Kanawaday, S., & Sane, A. (2018). Machine learning for predictive maintenance of industrial machines using IoT sensor data. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, 87-90.
Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., & Noh, S.D. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing Green Technology, 3 (1), 111-128.
Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M., & Sutherland, J.W. (2019). Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP, 80, 506-511.
Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random Forests. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 743 LNCS, 246-252.
Mitchell, R., & Frank, E. (2017). Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science 3: e127.
Mohammed, R., Rawashdeh, J., & Abdullah, M. (2020). Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results. 2020 11th International Conference on Information and Communication Systems, ICICS 2020, 243-248.
Schwendemann, S., Amjad, Z., & Sikora, A. (2021). A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines. Computers in Industry, 125, 103380.
Uddin, S., Khan, A., Hossain, M.E., & Moni, M.L. (2019). Comparing different supervised machine learning algorithms for disease Prediction. BMC Medical Informatics and Decision Making, 19 (1), 1-16.
Vieira, S., Lopez Pinaya, W.H., & Mechelli, A. (2019). Main concepts in machine learning. Machine Learning: Methods and Applications to Brain Disorders, 21-44.
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A Manufacturing Big Data Solution for Active Preventive Maintenance. IEEE Transactions on Industrial Informatics, 13 (4), 2039-2047.
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production and Manufacturing Research, 4 (1), 23-45.