Design of Embedded Motor Diagnostic System Based on Vibration Signal Analysis and Machine Learning

This paper introduces an embedded motor fault diagnosis system that utilizes vibration signal analysis and machine learning. The system is centered around the STM32H753 microcontroller and gathers real-time vibration signals from the motor using a vibration sensor. Signal features are extracted using Discrete Wavelet Transform (DWT) and combined with machine learning anomaly detection (AD) model for automatic monitoring of motor health status and fault identification. This system offers strong adaptability and high accuracy compared to traditional methods, making it well-suited for real-time application requirements in industrial sites.