全矢IMF信息熵用于高速列车转向架故障诊断  

Application of Full Vector IMF Entropy in Fault Diagnosis of High Speed Train

作　　者：李亚兰 金炜东[1] LI Yalan;JIN Weidong(School of Electrical Engineering,Southwest Jiaotong University Chengdu,611756,China)

机构地区：[1]西南交通大学电气工程学院,成都611756

出　　处：《振动．测试与诊断》2021年第5期874-879,1030,共7页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金重点资助项目(61134002)。

摘　　要：针对高速列车转向架振动信号具有非线性、非平稳的特征,以及单通道故障诊断带来的信息不完整问题,提出了一种多元经验模态分解(multivariate empirical mode decomposition,简称MEMD)和全矢本征模态函数(intrinsic mode function,简称IMF)信息熵相结合的高速列车故障特征提取方法。首先,使用MEMD方法对同源双通道的振动信号进行分解,得到一系列的2元本征模态函数;其次,分别计算前6个IMF的全矢IMF信息熵,通过特征评价方法进行特征维数约简;最后,将得到的特征向量作为支持向量机的输入来识别转向架的故障类型。实验结果表明,该方法能有效提高转向架的故障识别率,最高可达到100%,验证了全矢IMF信息熵在高速列车故障诊断中的可行性。Aiming at the nonlinear and non-stationary characteristics of high-speed train bogie vibration signal and incomplete information caused by single-channel fault diagnosis,a method is proposed combined multivariate empirical mode decomposition(MEMD)and full vector intrinsic mode function(IMF)information entropy to extract the feature of high-speed train. Firstly,the MEMD method is used to decompose the vibration signal of homologous dual channels,and a series of 2 dimensional intrinsic mode functions are obtained. Then,the full IMF information entropy of the first 6 IMFs is calculated respectively,and the feature dimension reduction is performed by feature evaluation method. Finally,the obtained feature vector is used as an input of the support vector machine to identify the fault type of the bogie. The experimental results show that the method can effectively improve the fault recognition rate of the bogie,up to 100%,and verify the feasibility of full-vector IMF information entropy in high-speed train fault diagnosis.

关 键 词：高速列车转向架 多元经验模态分解 本征模态函数 全矢IMF信息熵 特征评价 支持向量机 

分 类 号：TH165.3[机械工程—机械制造及自动化] TP206.3[自动化与计算机技术—检测技术与自动化装置]