Water leakage in pipeline systems causes significant water wastage, infrastructure damage, and financial losses across residential, industrial, and agricultural sectors. Conventional leakage detection methods rely on manual inspection and periodic monitoring, making them time-consuming and prone to delayed fault identification. This project presents a solar-powered AI-driven water leakage detection system for real-time pipeline monitoring and abnormality classification. The system employs ultrasonic, sound, and MPU6050 vibration sensors to continuously monitor pipeline conditions. Sensor data are acquired through an ESP32 microcontroller and transmitted for Artificial Intelligence-based analysis. The AI model classifies pipeline conditions as normal or abnormal and generates alerts when leakage is detected. The integration of solar power with a sun-tracking mechanism ensures reliable operation in remote and off-grid locations. The proposed system offers an energy-efficient, sustainable, intelligent, and cost-effective solution for smart water management and predictive maintenance.
Balakrishnan et al. (Thu,) studied this question.