噪声与振动控制2018，Vol.38Issue(3)：157-161,197,6.DOI:10.3969/j.issn.1006-1355.2018.03.030
基于MEEMD和GA-SVM的列车车轮多边形故障识别方法
Fault Diagnosis Method of Wheel Polygonization of Trains based on MEEMD and GA-SVM
摘要
Abstract
According to the non-steady characteristics of train wheel vibration signals, a diagnostic method based on modified ensemble empirical mode decomposition (MEEMD) and genetic algorithm support vector machine (GA-SVM) is proposed for identifying wheel polygonization faults. First of all, the vertical vibration signals of the wheel axle-box are decomposed into a number of intrinsic mode function (IMF) components by MEEMD. According to the kurtosis and energy values of the components, the main IMF components are selected. Then, Hilbert transform is used to obtain the envelope spectra of the main IMF components. And the envelope spectrum entropy values are calculated by the envelope spectrum. Finally, the normalized envelope spectrum entropy values are employed as the characteristic vector to input into the GA-SVM for training and testing. Analysis results of the measurement signals show that this method can effectively identify wheel polygonization fault and the recognition accuracy can reach 95％.关键词
振动与波/车轮多边形识别/改进的集合经验模态分解/遗传算法支持向量机/包络谱熵Key words
vibration and wave/wheel polygonization recognition/modified ensemble empirical mode decomposition (MEEMD)/genetic algorithm support vector machine (GA-SVM)/envelope spectrum entropy分类
信息技术与安全科学引用本文复制引用
陈博,陈光雄..基于MEEMD和GA-SVM的列车车轮多边形故障识别方法[J].噪声与振动控制,2018,38(3):157-161,197,6.基金项目
国家自然科学基金资助项目(51775461) （51775461）
