全矢样本熵在高速列车故障诊断中的应用

为了有效提取高速列车转向架振动信号的故障特征以及针对单通道采集的信息难以完善地反映出列车运行状态的问题,提出了一种基于全矢样本熵(full vector sample entropy,简称FVSE)算法的故障特征提取方法。首先,使用噪声辅助多元经验模态分解(noise assisted multivariate empirical mode decomposition,简称NAMEMD)方法对振动信号进行分解,得到一系列多元本征模态函数;其次,根据相关系数法选择与原始信号最相关的本征模态函数分别进行样本熵和全矢样本熵特征提取;最后,将得到的特征向量分别作为支持向量机的输入对列车状态进行识别。实验结果表明,采用全矢样本熵算法的故障识别率普遍比采用样本熵算法提高了6个百分点,最高达到了98%以上,验证了噪声辅助多元经验模态分解方法结合全矢样本熵算法对高速列车故障诊断的有效性。

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

In order to effectively extract the fault characteristics of the high-speed train bogie vibration signal and aim at the problem of the information of single-channel acquisition is difficult to fully reflect the running state of the train, a new fault feature extraction method based on full vector sample entropy (FVSE) algorithm is proposed. Firstly, the noise assisted multiempirical mode decomposition (NAMEMD) method is used to decompose the vibration signal to obtain a series of multi-intrinsic mode functions. Then, according to the correlation coefficient method, the most relevant intrinsic mode functions to the original signal are selected to calculate the sample entropy and the full vector sample entropy. Finally, the support vector machine is used to identify the train status. The experimental results show that the recognition rate of the train using the FVSE algorithm is generally 6% higher than the sample entropy algorithm, and the highest is above 98%, the validity of noise assisted multivariate empirical mode decomposition and full vector sample entropy for fault diagnosis of high speed trains is verified.