The detection results need to be analyzed and distinguished by professional technicians in the fault detection methods for induction motors based on signal processing and it is difficult to realize the automatic identification of stator and rotor faults. A method for identifying stator and rotor faults of induction motors based on machine vision is proposed to solve this problem. Firstly, Park’s vector approach (PVA) is used to analyze the three-phase currents of the motor to obtain Park’s vector ring (PVR). Then, the local binary patterns (LBP) and gray level cooccurrence matrix (GLCM) are combined to extract the image features of PVR. Finally, the vectors of image features are used as input and the types of induction motor faults are identified with the help of a random forest (RF) classifier. The proposed method has achieved high identification accuracy in both the Maxwell simulation experiment and the actual motor experiment, which are 100% and 95.83%, respectively.

中文翻译：

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基于机器视觉的感应电动机定，转子故障识别方法

在基于信号处理的感应电动机故障检测方法中，检测结果需要由专业技术人员进行分析和区分，难以实现定子和转子故障的自动识别。为了解决这个问题，提出了一种基于机器视觉的感应电动机定，转子故障识别方法。首先，采用Park矢量法（PVA）分析电动机的三相电流，以获得Park矢量环（PVR）。然后，将局部二进制模式（LBP）和灰度共生矩阵（GLCM）组合在一起，以提取PVR的图像特征。最后，将图像特征向量用作输入，并借助随机森林（RF）分类器识别感应电动机故障的类型。