Machine Learning-Driven Preventive Maintenance for Fibreboard Production in Industry 4.0

The transition to Industry 4.0 has necessitated the adoption of intelligent maintenance strategies to enhance manufacturing efficiency and reduce operational disruptions. In fibreboard production, conventional preventive maintenance, reliant on fixed schedules, often leads to inefficient resource allocation and unexpected failures. This study proposes a machine learning-driven predictive maintenance (PdM) framework that utilises real-time sensor data and predictive analytics to optimise maintenance scheduling and improve system reliability. The proposed approach is validated using real-world industrial data, where Random Forest and Gradient Boosting regression models are applied to predict machine wear progression and estimate the remaining useful life (RUL) of critical components. Performance evaluation shows that Random Forest outperforms Gradient Boosting, achieving a lower Mean Squared Error (MSE) of 0.630, a lower Mean Absolute Error (MAE) of 0.613, and a higher R-squared score of 0.857. Feature importance analysis further identifies surface grade as a key determinant of equipment wear, suggesting that redistributing production across lower-impact grades can significantly reduce long-term wear and extend machine lifespan. These findings underscore the potential of artificial intelligence in predictive maintenance applications, contributing to the advancement of smart manufacturing in Industry 4.0. This research lays the foundation for further investigations into adaptive, real-time maintenance frameworks, supporting sustainable and efficient industrial operations.