IoT-Based Predictive Maintenance and Monitoring System for Water Purifiers

This project focuses on developing an Internet of Things (IoT) based predictive monitoring and maintenance system for water purifiers. Various sensors such as pressure, pH, vibration, temperature, flow, and turbidity sensors are integrated into the purifier to continuously monitor the condition of important components like filters and pumps. Machine learning models including Long Short-Term Memory (LSTM) and Gradient Boosting algorithms such as XGBoost are used to analyze sensor data and identify possible system failures or performance degradation in advance. The proposed system provides real-time notifications through a user interface, allowing users to take preventive maintenance actions before major faults occur. As a result, the lifespan of the purifier can be extended, maintenance costs can be reduced, and a continuous supply of clean drinking water can be ensured.