Estimation of Remaining Useful Life of Equipment and its Role in the Optimization of Predictive Maintenance

In predictive maintenance, estimating the remaining useful life (RUL) of equipment and machines is essential to plan maintenance, optimize efficiency and avoid unplanned downtime. RUL refers to the estimated duration an asset can continue to operate effectively before it requires repair or replacement. It serves as a key indicator for optimizing maintenance schedules, improving asset utilization, and sustaining overall plant efficiency. In the manufacturing industry, where even minor disruptions can result significant production losses, reliable RUL estimation is crucial for maintaining workflow continuity and product quality. This study highlights the crucial importance of estimating RUL in manufacturing systems, reviews recent advances in prognostic methodologies and addresses the limitations of purely data-based or physics-based approaches by proposing a hybrid RUL estimation framework. The proposed method integrates statistical reliability measures to allow more accurate and robust predictions in dynamic industrial environments. The results obtained validate the proposed methodology and demonstrate its effectiveness in improving the accuracy and robustness of the RUL estimation.