Abstract
This paper proposes the design and implementation of an intelligent control system based on machine learning algorithms for the predictive maintenance of industrial equipment. The system is developed on a data-driven pipeline comprising sensor data collection, feature engineering through rolling statistics, and regression-based Remaining Useful Life (RUL) estimation. As a dataset for methodology the NASA C-MAPSS was used for evaluating performance. During the designing of an intelligent assistance, some machine learning models were implemented and compared, such like Random forest model, XGBoost model, LightGBM model, and Long Short-Term Memory. Then it was calculated by MAE, RMSE, R² indicators. The outcomes identified that LightGBM achieves the best balance and performance between predictive and computational expense, but LSTM model had some struggles with overfitting and resource utilization. The system architecture suggested here is scalable and modular and thus would be appropriate for real-time industrial control applications. The study gives a comprehensive performance comparison of the model and explains a pragmatic system for integrating machine learning and intelligent control systems for predictive maintenance and operational reliability.