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
Abstract 67 | PDF Downloads 42

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Keywords

Bearing Fault Detection, Machine Learning, Condition Monitoring

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Section
Poster Presentations