张昊宇,姚钢,殷志柱,周荔丹.基于小波神经网络与KNN机器学习算法的六相永磁同步电机故障态势感知方法[J].电测与仪表,2019,56(2):1-9. Zhang Haoyu,Yao Gang,Yin Zhizhu,Zhou Lidan.Fault State Perception Method for Six Phase PMSM Based on Wavelet Neural Network and KNN Machine Learning Algorithm[J].Electrical Measurement & Instrumentation,2019,56(2):1-9.
基于小波神经网络与KNN机器学习算法的六相永磁同步电机故障态势感知方法
Fault State Perception Method for Six Phase PMSM Based on Wavelet Neural Network and KNN Machine Learning Algorithm
In order to avoid the more serious motor fault and system breakdown which were caused by open phase during six-phase permanent magnet synchronous motor (PMSM) running, it is necessary to do the fault prediction and diagnosis. This paper derived mathematical model of neutral point isolation six-phase PMSM shifted by 30° with the principle of stator magnetomotive force invariance. Wavelet packet analysis was used to collect the feature values and wavelet neural network was built to do the fault prediction and avoid system spurious triggering. The K-Nearest Neighbor (KNN) machine learning system had also been built to diagnose the fault types quickly which could realize fault state perception. MATLAB and Scikit-Learn library of Python were used to do simulations which could verify the strategy reliable and effective.
