Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning
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Published
Nov 5, 2024
Victor Bauler
Júlio Cordioli
Danilo Silva
Danilo Braga
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
Bauler, V., Cordioli, J., Silva, D., & Braga, D. (2024). Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4065
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