电气传动2018，Vol.48Issue(4)：70-74,80,6.DOI:10.19457/j.1001-2095.20180415
基于电流时频特征的不对中故障诊断研究
Research on Misalignment Fault Diagnosis Method Based on Time-frequency Characteristics of Current
摘要
Abstract
Motor current signal analysis has been an effective way to monitor and diagnose electrical machines. However,little research work has been reported in using this technique for rotor systems. In order to diagnose the misalignment fault of rotor system,a method was proposed which included the empirical model decomposition(EMD) and genetic algorithm optimization support vector machine(GA-SVM). First,the EMD was used to decompose the current signal into several IMFs.Then,the energy characteristics and kurtosis of each IMF component were calculated. Finally,the energy features and kurtosis of the IMFs containing the fault information were input to the GA-SVM for fault classification and recognition. The experimental results show that this method can effectively diagnose the misalignment fault type and fault degree of rotor system.The method can improve the correct rate of the fault diagnosis, compared with the method that only depends on the EMD energy characteristic.关键词
不对中/电机电流/时频特征/经验模态分解Key words
misalignment/motor current/time-frequency characteristic/empirical mode decomposition(EMD)分类
机械制造引用本文复制引用
李峰,庞新宇,杨兆建..基于电流时频特征的不对中故障诊断研究[J].电气传动,2018,48(4):70-74,80,6.基金项目
国家自然科学基金项目资助(51475318) （51475318）
山西省研究生教育创新项目(2016BY058) （2016BY058）
