IMPLEMENTATION OF IOT-BASED PREDICTIVE MAINTENANCE FOR INDUCTION MOTORS IN INDUSTRIAL ENTERPRISES

Abstract

Induction motors account for approximately 40–45% of global industrial electricity consumption. Unplanned downtime due to stator faults, bearing failures, and rotor imbalances costs manufacturing enterprises an estimated $15–20 billion annually. This paper presents a complete Internet of Things (IoT) based predictive maintenance system for real-time condition monitoring of three-phase induction motors. The proposed system integrates low-cost wireless vibration sensors, thermal imaging modules, and current signature analyzers connected via a LoRaWAN gateway to a cloud-based analytics platform. A two-year study was conducted across 48 industrial motors (5.5–250 kW) operating in a German automotive parts factory. Using machine learning algorithms (random forest and long short-term memory networks), the system achieved 94.7% accuracy in fault prediction with an average warning time of 312 hours before critical failure. The results demonstrate a 67% reduction in unplanned downtime, a 53% decrease in maintenance costs, and a payback period of 1.2 years. The proposed solution prevents 82 tonnes of CO₂ equivalent per facility per year by reducing production stoppages and avoiding premature motor replacement. This scalable approach can be implemented across various industrial sectors.