Friction-Based On-Line Health Assessment and Predictive Maintenance for Belt Drive System

Belt drive systems are integral to many industrial machines, but their deterioration can compromise product quality and lead to mechanical damage. Traditional belt health assessment methods often suffer from limited accuracy or require manual intervention. This research proposes a novel approach to belt drive system health assessment and predictive maintenance (PdM). By integrating physical friction models with data-driven techniques, we achieve accurate and interpretable health assessments for timing belts within permanent-magnet synchronous motors (PMSMs). Our method avoids the need for additional sensors, ensuring cost-effectiveness. It also merges predictive analytics, identifying potential failures, with prescriptive analytics, suggesting the most effective maintenance actions. This combined approach helps to minimize downtime, optimize component lifespans, and reduce the risk of unexpected breakdowns. The proposed methodology offers a compelling solution for industries seeking to enhance the reliability and efficiency of their belt-driven machinery.