Iot-based condition monitoring system for predictive maintenance of induction motors / Cristy H. Abion, [and 4 others].

Undergraduate Thesis (BSME) - Mechanical Engineering. Camarines Sur Polytechnic Colleges, 2024.

Includes bibliographic references.

The reliability of induction motors is crucial in industrial operations, as unexpected failures can lead to costly downtimes and production losses. This study presents an IoT-Based Condition Monitoring System designed for the predictive maintenance of induction motors, leveraging real-time data collection and cloud-based analysis. The system employs temperature and current sensors interfaced with a NodeMCU v3 microcontroller, transmitting data to a MySQL cloud database for monitoring and fault prediction. By integrating predictive analytics, the system can detect anomalies and schedule maintenance proactively, reducing unplanned breakdowns. Experimental results demonstrate that the proposed system enhances fault detection accuracy, improves maintenance planning, and extends motor lifespan. Compared to traditional monitoring methods, the loT-based approach ensures higher reliability, reduced maintenance costs, and improved operational efficiency. This research contributes to the advancement of smart industrial automation by providing a scalable, cost-effective solution for predictive maintenance in various industries.