An Intelligent Machine Learning Framework for Predictive Maintenance in Smart Manufacturing Systems
Authors-P.Ramakrishna, T.Sai Nikitha
Keyword-Predictive Maintenance, Machine Learning, Industry 4.0, Random Forest, XGBoost, Support Vector Machine, Equipment Failure Prediction, Industrial Automation, Data Analytics, Condi-tion Monitoring.
The growing for intelligent maintenance strategies capable of preventing unexpected equipment failures. Predictive maintenance has emerged as an effective solution by utilizing historical op-erational data and machine learning techniques to estimate machine health before critical failures occur. This research presents a comprehensive The proposed framework employs Random Forest, Extreme Gradient Boosting (XGBoost), to analyze operational sensor measurements and identify possible machine failures. collected from industrial machines. Data preprocessing in-volves label encoding, feature scaling, train-test splitting, and robust normalization before model development. Five-feffectively minimizes unexpected downtime, improves maintenance schedul-ing, lowers operational costs, and enhances manufacturing productivity. These findings indicate that supervised machine learning provides a reliable and scalable solution for intelligent mainte-nance planning across modern industrial environments.
Doi-[https://doi.org/10.5281/zenodo.21735759]