Fault detection system for stepping motors based on K-means clustering

Stepper motors are susceptible to a variety of faults during operation, which can compromise their performance and potentially lead to system shutdown or damage. Such issues can result in production interruptions and increased maintenance costs. This paper introduces a novel method for detecting stepper motor faults, utilizing the K-means clustering algorithm in conjunction with vibration sensors. By capturing vibration signals from the stepper motor and processing them via a microcontroller, we achieve real-time monitoring and classification of potential faults. Experimental results demonstrate that this method yields effective clustering outcomes, offering a promising solution for stepper motor fault detection.