Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In recent years, the application of machine learning techniques in condition monitoring has significantly advanced the precision and efficiency of fault detection processes. In particular, detecting bearing faults in conveyor belt drums is critical in the mining industry for maintaining operational reliability and productivity. This paper presents a case study using vibration signals and diagnostic reports provided by the company Dynamox. After meticulous data cleaning, preprocessing, and feature extraction employing advanced signal processing techniques and statistical features, several machine learning models were trained, optimized and evaluated, with the best models providing very promising results.
How to Cite
##plugins.themes.bootstrap3.article.details##
Bearing Fault Detection, Machine Learning, Condition Monitoring
Carvalho, Y. S. C. d. (2022). Failure detection in belt conveyor drum bearings using accelerometers and machine learning. (Unpublished master’s thesis). Federal University of Ouro Preto.
Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (p. 785–794). doi: 10.1145/2939672.2939785
Geron, A. (2022). Hands-on machine learning with scikit- learn, keras, and tensorflow. O’Reilly.
Hendriks, J., Dumond, P., & Knox, D. A. (2022, April). Towards better benchmarking using the cwru bearing fault dataset. Mechanical Systems and Signal Processing, 169, 108732. doi: 10.1016/j.ymssp.2021.108732
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Randall, R. B. (2021). Vibration-based condition monitoring (2nd ed.). Wiley.
Swinderman, R. T. (2009). Foundations: The practical resource for cleaner, safer, more productive dust and material control (4th ed.).
Watt, J., Katsaggelos, A. K., & Borhani, R. (2016). Machine learning refined: foundations, algorithms, and applications. Cambridge: Cambridge university press.
Zimroz, R., & Krol, R. (2015, September). Failure analysis of belt conveyor systems for condition monitoring purposes. Mining Science, 128(36), 255–270.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.