Integrating Machine Learning-Based Remaining Useful Life Predictions with Cost-Optimal Block Replacement for Industrial Maintenance

Abstract

This study presents a preventive maintenance methodology to predict the remaining useful life (RUL) of mechanical systems and determine cost-effective replacement schedules. The approach integrates machine learning for RUL prediction, Weibull distribution for reliability analysis, and a block replacement model with minimal repair to optimize preventive maintenance. Many existing studies rarely incorporate RUL prediction results into determining optimal maintenance actions due to the high uncertainty in RUL prediction. To address this, the proposed methodology emphasizes not stopping at the prediction stage but integrating RUL predictions into actionable maintenance strategies to reduce uncertainty and improve applicability in industrial contexts. A case study using the open CMAPSS dataset demonstrates the feasibility of the approach. The value of this study lies in proposing a methodology that not only utilizes prediction-based proactive outcomes instead of predefined replacement intervals but also integrates them with subsequent maintenance strategies, providing practical and cost-effective solutions for industrial applications.