AIM-MC: Artificial Intelligence and Machine Learning for Motor Condition Monitoring on STM32 IoT Platforms
DOI:
https://doi.org/10.15662/IJEETR.2026.0802077Keywords:
Condition-Based Monitoring, Fault Diagnosis, Industrial Electric Motors, Vibration Signal Analysis, Current Monitoring, TinyML, Edge ComputingAbstract
Industrial motors operate continuously under harsh mechanical and electrical condi- tions, making them vulnerable to faults such as bearing wear, shaft misalignment, and overload. Undetected faults result in downtime, increased maintenance costs, and safety risks. This paper presents a low-cost AI and TinyML-based Condition-Based Monitoring (CBM) system for industrial motors using vibration and current analysis. The proposed system employs an ADXL345 accelerometer for vibration sensing and an ACS712 current sensor for overload detection. Time-domain vibration features, specifically RMS values, are extracted and processed using a rule-based algorithm and a TinyML model trained us- ing Edge Impulse. An STM32 microcontroller performs real-time fault classification, while fault status is transmitted to the Blynk IoT platform via a NodeMCU (ESP8266) module. Experimental results show that the proposed system achieves high fault detection accuracy with low computational overhead, making it suitable for Industry 4.0 applications.
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