Bearing Fault Detection in Conveyor Belt Drums Using Machine Learning
Year: 2024
Type: conference-paper
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.
Institution: Universidade Federal de Santa Catarina
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404289666