Review on IoT-Enhanced Predictive Maintenance and Safety for Energy Generating Facilities

Ensuring the safety and operational efficiency of energy generation facilities, particularly within the renewable energy sector, is increasingly essential due to complex system demands and challenging environmental conditions. This paper introduces an IoT-based predictive maintenance (PM) and safety monitoring system designed to optimize equipment reliability and reduce unplanned downtime in energy facilities. The proposed system leverages a network of sensors, including the DHT22, MPU-6050, INA219, BMP280, and Magnetic Hall Effect sensors, to capture real-time data on temperature, humidity, vibration, current, and speed. An ESP32 microcontroller is used for centralized data acquisition, and data is processed through AWS IoT services for storage, analysis, and remote monitoring. By analysing trends in historical and live sensor data, the system can predict potential equipment failures, allowing for proactive maintenance measures. Additionally, integrated safety features enable remote shutdowns and automated alerts when critical thresholds are exceeded. Field tests validate the system’s effectiveness in improving safety standards and optimizing maintenance schedules, offering a scalable, cost-effective solution for enhanced reliability in the energy generation industry.