A Review on Digital Twin with AI Based Predictive Maintenance System

Predictive maintenance reduces unexpected machine failures and downtime by continuously observing equipment condition and identifying abnormal behaviour before severe failure occurs. This review paper examines recent research on Digital Twin (DT), artificial intelligence (AI), machine learning, multi-sensor condition monitoring, and predictive maintenance of industrial machines, with emphasis on induction motors. Based on the reviewed literature, a low-cost integrated architecture is proposed using temperature, vibration, current, and RPM sensing, an ESP32 controller, AI-based fault prediction, Digital Twin updating, and IoT-based monitoring. Random Forest is considered as the main classification model because related literature demonstrates strong performance for motor-fault classification. Sample formulas for RMS vibration, mean, variance, RPM, and percentage change are presented. The proposed workflow connects physical sensing, data processing, AI prediction, virtual representation, alert generation, and predictive-maintenance decision support. Illustrative graphs are included only as sample results and must be replaced by measured experimental data before final validation.