PREDICTIVE MAINTENANCE USING MACHINE LEARING

Predictive maintenance has become an important strategy in modern industrial environments for improving operational efficiency, reducing equipment downtime, and minimizing unexpected machine failures. Traditional maintenance approaches such as reactive maintenance and timebased scheduled maintenance are often inefficient because they either address problems after failures occur or result in unnecessary servicing of equipment. With the rapid growth of Industrial Internet of Things (IIoT), sensor technologies, and data analytics, industries can now collect large volumes of real-time equipment data. Machine learning techniques provide advanced tools for analyzing this data and predicting potential failures before they occur. In this study, a predictive maintenance system based on machine learning algorithms is proposed to detect equipment degradation patterns and forecast potential failures using historical sensor data. The system uses the XGBoost algorithm, a powerful gradient boosting model capable of handling nonlinear relationships and large datasets effectively. The methodology involves data preprocessing, feature engineering, anomaly detection, model training, and performance evaluation. Sensor variables such as temperature, vibration, rotation speed, and pressure are used as input features for the predictive model. The trained model evaluates equipment health status and estimates the probability of failure. Performance metrics including accuracy, F1-score, ROC-AUC, and RMSE are used to evaluate model effectiveness. The proposed system demonstrates improved predictive capabilities and supports proactive maintenance planning in industrial systems. By enabling early detection of equipment faults, organizations can reduce maintenance costs, improve productivity, and enhance system reliability. The integration of machine learning with industrial monitoring systems provides a scalable and intelligent framework for predictive maintenance applications.