This research develops a predictive maintenance framework for hydraulic systems by utilizing Internet of Things (IoT) technology and machine learning. Hydraulic systems often experience unexpected failures, causing expensive downtime and disrupting industrial operations. By installing IoT sensors, data about system performance and condition can be collected in real-time. This data is analyzed using machine learning algorithms to detect patterns and signs of possible failure. The proposed framework enables early detection of problems and provides timely maintenance recommendations, improving operational efficiency and reducing maintenance costs. Test results show that this approach can improve the reliability of the hydraulic system and extend the service life of the equipment. This research makes a significant contribution to the development of innovative, data-driven maintenance solutions for industry.
