Predictive maintenance for industrial motors using embedded system

Industrial motors are widely used in manufacturing plants, pumps, conveyors, compressors and other industrial equipment. Unexpected motor failure can interrupt production, increase maintenance costs and create safety risks. Routine maintenance and reactive repair may not identify developing faults early enough. This project presents an embedded-system-based predictive maintenance system for industrial motors that monitors operating parameters and provides early warnings when abnormal conditions are detected. The proposed system uses an ESP32 microcontroller with a DHT11 temperature and humidity sensor, an SW-420 vibration sensor and an ACS712 current sensor. Sensor readings are processed by the controller to identify conditions such as excessive temperature, abnormal vibration and unusual current consumption. An LCD with an I2C interface displays the operating status, while LEDs and a buzzer provide local fault alerts. The ESP32 can also transmit readings to an IoT dashboard for remote monitoring. An AI prediction layer can be added by collecting labelled operating data and training a classification or anomaly-detection model to estimate motor condition. In a small-scale implementation, threshold-based alerts provide the baseline, while the AI model can be evaluated separately using recorded data. The project demonstrates how embedded sensing, IoT communication and data-driven analysis can support condition monitoring and reduce unplanned downtime. The system is intended as an educational demonstrator and requires industrial-grade sensors, isolation and validation before deployment on production equipment.