基于自适应滑窗的开关磁阻电机调速系统故障诊断

Fault Diagnosis of Switched Reluctance Motor Drive Based on Adaptive Sliding Window

针对开关磁阻电机调速系统(SRD)中功率变换器单管短路故障和传感器噪声故障,提出将k-邻近算法(kNN)和极限学习机(ELM)相结合的自适应滑窗故障诊断方法。通过对故障进行分析,采集三相定子电流作为原始数据,将快速傅里叶变换和ReliefF算法用于特征提取与选择,形成kNN算法与ELM算法相结合的多窗口自适应故障诊断机制。通过离线与在线仿真实验,证明了该方法诊断速度快,精度高。

Aiming at the single-tube short-circuit fault and sensor noise fault of power converter in switched reluctance motor drive(SRD),an adaptive sliding window fault diagnosis method combining k-nearest neighbor(k NN)algorithm and extreme learning machine(ELM)was proposed.The fault was analyzed and the three-phase stator current were collected as the original data.The fast fourier transform and ReliefF algorithm were used for feature extraction and selection.A multi-window adaptive fault diagnosis structure combining k NN algorithm and ELM algorithm was set up.Through offline and online simulation experiments,it is proved that the method has high diagnostic speed and high precision.