Published Paper Details:

Due to their high efficiency, dependability, and low maintenance requirements, brushless DC (BLDC) motors are utilized extensively in industrial automation, electric vehicles, and consumer electronics. However, undetected faults in these motors, such as phase imbalance, bearing wear, rotor misalignment, or overheating--can lead to system failures and costly downtimes. Utilizing the potent capabilities of the ESP32 microcontroller and Internet of Things (IoT) technology, this project proposes an advanced fault detection and identification (FDI) system for BLDC motors. The system continuously monitors key motor parameters such as voltage, current, temperature, and vibration using a combination of analog and digital sensors. Real-time signal processing, anomaly detection, and wireless communication all make use of the ESP32. The microcontroller's lightweight machine learning algorithms and threshold-based logic identify fault conditions, ensuring prompt and accurate fault recognition. Detected faults and operational data are transmitted via Wi-Fi or MQTT protocols to a cloud-based IoT dashboard, enabling real-time remote monitoring, data visualization, and timely notifications through web or mobile interfaces. This approach supports predictive maintenance and enhances overall system reliability. Experimental testing on a BLDC motor testbed substantiates the implementation's efficacy in early fault detection, low-latency communication, and seamless IoT platform integration. The project demonstrates the potential for next-generation motor health monitoring solutions by combining smart diagnostics with embedded IoT.