A Scalable IoT-Based Framework for Predictive Maintenance of Industrial Electrical Equipment

Abstract

Modern industrial operations are highly dependent on electrical equipment, where unforeseen failures can result in considerable economic losses and safety hazards. This research introduces an IoT-based framework for the remote monitoring of electrical devices, aimed at facilitating predictive maintenance to minimize operational downtime and associated costs. The proposed approach integrates advanced sensor technologies with cloud computing to continuously collect real-time data on critical parameters such as insulation resistance and temperature. This data is then analyzed using automated algorithms for fault detection and maintenance scheduling. Case study findings validate the system’s capability in early fault identification and improving maintenance efficiency. The originality of this work lies in its scalable and cost-effective design, which offers a practical alternative to traditional maintenance strategies. The proposed solution has significant implications for industrial maintenance, providing a proactive means to enhance equipment reliability and operational safety.