Forecasting Remaining Usable Life of Vehicle Bearings for Enhanced Production and Reduced Maintenance Costs
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Abstract
Bearings play a crucial role in vehicle structures and systems, enabling relative motion between essential components such as shafts and housings. However, during service, many bearings are prone to failure due to factors like excessive loading, improper lubrication and ineffective sealing. To optimize production and minimize maintenance costs, accurately forecasting a bearing's Remaining Usable Life (RUL) becomes essential. This study focuses on three different deep groove ball bearings used in diverse vehicle applications, including washing machines, electric motors, gear drives and pumps. Utilizing Python programming language and input parameters such as radial and axial loads, speed and shift hours, RUL of the bearings was calculated. The results demonstrated a strong correlation between RUL and shift hours as well as the load acting on the bearings. Notably, a 25% increase in bearing RUL was observed with a decrease in shift hours, facilitating informed decisions on optimal machine operating hours and cost-effective maintenance strategies for vehicle systems.
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