In this paper, a method for detecting the stator internal coil fault detection for a permanent magnet synchronous motor (PMSM) using the ANFIS algorithm is proposed and described. At first, the dynamic model of the synchronous motor along with its certain fault will be introduced. Since fault detection in these engines is very important and has a high value, different methods have been proposed for detecting stator deflection in electric machines. To determine the fault percentage in the permanent magnet synchronous motor, a neuro-fuzzy adaptive inference system is used to identify the fault. The advantages of the proposed algorithm are the ability to detect faults with different domains. It is flexible enough to be used for offline and online identification. For this reason, we have used neuro-comparative learning techniques in fuzzy logic in this paper. The inputs of the proposed algorithm are two PMSM current and torque signals in normal and faulty conditions. In the proposed algorithm, the membership function structure was created with the fuzzy C-means clustering method. The simulation results show that the proposed algorithm can accurately determine where and with what speed the fault occurs.

本文提出并描述了一种使用ANFIS算法检测永磁同步电动机（PMSM）定子内部线圈故障的方法。首先，将介绍同步电动机的动态模型及其某些故障。由于这些发动机中的故障检测非常重要并且具有很高的价值，因此提出了用于检测电机中的定子挠度的不同方法。为了确定永磁同步电动机中的故障百分比，使用神经模糊自适应推理系统来识别故障。该算法的优点是能够检测不同域的故障。它足够灵活，可以用于脱机和联机标识。为此原因，我们在模糊逻辑中使用了神经比较学习技术。该算法的输入是正常和故障条件下的两个PMSM电流和转矩信号。在提出的算法中，使用模糊C-均值聚类方法创建了隶属函数结构。仿真结果表明，该算法能够准确确定故障发生的位置和速度。