ABSTRACT:

Submersible motors are critical components in agriculture, water management, and industrial applications, yet they suffer frequent failures due to harsh operating conditions and inadequate maintenance. This paper presents the design and implementation of an intelligent preventive maintenance kit utilizing Internet of Things (IoT) sensors, edge computing, and cloud analytics for real-time condition monitoring and predictive maintenance of submersible motors. The proposed system employs a NodeMCU ESP8266 microcontroller integrated with multiple sensors to continuously monitor motor health parameters including temperature (DS18B20), vibration (MEMS accelerometer), voltage, flow rate, and RPM. Data is wirelessly transmitted to a cloud platform where machine learning algorithms analyse patterns and detect anomalies. Experimental results demonstrate the system’s capability to identify early signs of insulation degradation, bearing wear, cavitation, and electrical imbalances, thereby reducing unplanned downtime by up to 40% and extending motor lifespan. The kit is low-cost, user-friendly, and deployable in remote areas, making it a practical solution for farmers and small-scale industries. This work contributes to the growing field of IoT-based predictive maintenance and provides a feasible model for enhancing the reliability and efficiency of submersible motor systems.