Machine learning for predictive maintenance: a binary classification problem
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Published:
May 11, 2022
Keywords:
Machine learning, predictive maintenance, fault detection, binary classification
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Abstract
Introduction. With the rise of Industry 4.0, a large amount of data is being extracted from machines and processes, which can be analyzed using machine learning approaches, allowing for more reliable decision making within the maintenance area; performing predictive maintenance data analysis becomes a challenge for a human being due to the large amount of data. Objective. For this reason, the objective of this study is to create a predictive machine learning model to detect failures. Methodology. The ai4i2020 predictive maintenance data available in the Machine Learning repository of the University of California and the free Python software were used to create the model. Four classification algorithms were evaluated to compare them based on performance metrics. Results. As a result, SVM is the best algorithm with an accuracy of 98.95% and a precision of 98.88% (optimized hyperparameters). Conclusions. It is concluded that the model works with high performance and good generalization of patterns learned during training, on test data or data not seen by the algorithm.
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Vilema Lara, P. H., García Mora, F. A., & Gallegos Londoño, C. M. (2022). Machine learning for predictive maintenance: a binary classification problem. ConcienciaDigital, 5(2.1), 45-68. https://doi.org/10.33262/concienciadigital.v5i2.1.2150
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References
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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.