机电工程技术2024，Vol.53Issue(4)：307-311,5.DOI:10.3969/j.issn.1009-9492.2024.04.067
基于高频电流信号的电机故障特征提取方法
Motor Fault Feature Extraction Method Based on High Frequency Current Signal Demodulation
摘要
Abstract
The asynchronous motor fault diagnosis mainly depends on the parameters such as vibration,temperature and noise,the monitoring of electrical signals,it is mainly used for analyzing the motor output power.In addition to the power output of the motor,the electrical signal also contains a wealth of mechanical fault,electrical fault and other information characteristics.Because the electrical signal in the fault characteristic signal is weak,easily lost to the fundamental frequency component and noise is to highlight the fault characteristic,is not conducive to machine condition monitoring and fault diagnosis.A method based on high frequency current signal demodulation of motor fault feature extraction method is proposed,in view of the sampling rate is not lower than 25.6 kHz high frequency current signal of motor stator,three analysis methods of high-pass filter,Hilbert transform and fast Fourier transform(FFT)are used to extract the weak characteristic signals of motor faults in high frequency current.The neural network algorithm is used to diagnose the motor normal,static eccentricity,dynamic eccentricity,rotor broken strip,bearing inner ring and bearing outer ring,and the effectiveness of the method is verified.The fault characteristics of current signal are extracted accurately and six kinds of fault are identified successfully.关键词
异步电机/故障诊断/Hilbert变换/高频电流信号/神经网络Key words
asynchronous motor/fault diagnosis/Hilbert transform/high frequency current signal/neural network分类
机械制造引用本文复制引用
杨磊,郭莉侠,王亚东,雷成,李亮,杜宗阳..基于高频电流信号的电机故障特征提取方法[J].机电工程技术,2024,53(4):307-311,5.基金项目
江苏核电有限公司科研项目—核电站用电机健康管理系统研究(JNPC-KY-201933) （JNPC-KY-201933）
