Edge AI-Driven Motor Health Prognostics and Autonomous Power Continuity Management System Using ESP32

The Edge AI-Driven Motor Health Prognostics and Autonomous Power Continuity Management System Using ESP32 is an advanced Industry 4.0-oriented intelligent monitoring and control platform designed to improve the reliability, safety, and operational continuity of DC motor-driven systems. The proposed system combines Edge Artificial Intelligence (AI), predictive maintenance, hybrid energy management, and autonomous fault recovery within a compact embedded architecture.

The system continuously monitors motor operating parameters such as supply voltage, battery status, motor operating conditions, relay status, and power availability. An ESP32 microcontroller acts as the edge computing unit, locally processing sensor and operational data to identify abnormal motor behavior and predict potential failures before they occur.

A lightweight Edge AI algorithm analyzes trends in motor performance and power system conditions to estimate motor health and detect early warning signs of faults such as power interruptions, abnormal loading conditions, voltage instability, and operational degradation. When a fault condition is predicted, the system automatically initiates protective actions and generates alerts through a buzzer and OLED display.

To ensure uninterrupted operation, the system incorporates a hybrid power continuity architecture consisting of an AC-powered SMPS and a rechargeable 3-cell lithium battery pack protected by a BMS. During mains power failure, the system autonomously switches to battery backup using relay-based power management, allowing continuous motor operation without human intervention.

The OLED display provides real-time visualization of motor health, battery condition, operational mode, and fault status. Through ESP32 wireless connectivity, maintenance personnel can remotely monitor equipment health and receive predictive maintenance notifications via IoT platforms.

The proposed framework transforms conventional motor control systems into intelligent cyber-physical assets capable of self-monitoring, self-protection, and autonomous power continuity management, thereby reducing downtime, improving reliability, and extending equipment lifespan.