The detection and identification of induction motor faults using machine learning and signal processing is a valuable approach to avoiding plant disturbances and shutdowns in the context of Industry 4.0. In this work, we present a study on the detection and identification of induction motor faults using machine learning and signal processing with MATLAB Simulink. We developed a model of a three-phase induction motor in MATLAB Simulink to generate healthy and faulty motor data. The data collected included stator currents, rotor currents, input power, slip, rotor speed, and efficiency. We generated four faults in the induction motor: open circuit fault, short circuit fault, overload, and broken rotor bars. We collected a total of 150,000 data points with a 60-40% ratio of healthy to faulty motor data. We applied Fast Fourier Transform (FFT) to detect and identify healthy and unhealthy conditions and added a distinctive feature in our data. The generated dataset was trained different machine learning models. On comparing the accuracy of the models on the test set, we concluded that the Decision Tree algorithm performed the best with an accuracy of about 92%. Our study contributes to the literature by providing a valuable approach to fault detection and classification with machine learning models for industrial applications.

使用机器学习和信号处理来检测和识别感应电动机故障是工业4.0背景下避免生产中断和停机的一种有价值的方法。本研究运用MATLAB Simulink进行感应电动机故障的检测和识别研究，开发了一个三相感应电动机模型，收集了正常和故障电机数据。我们应用快速傅里叶变换(FFT)检测健康和不健康状态，并使用决策树算法进行模型训练，最终得到约92%的准确率。这项研究为工业应用提供了具有价值的故障检测和分类方法。

应用机器学习和信号处理技术进行感应电机故障诊断