基于EEMD排列熵的高速列车轮对轴承故障诊断方法

摘要

The safety and reliability of high speed train axle bearing is of great significance to the safe operation of high-speed train and its fault feature is mainly reflected in the vibration signal of the axle box. In this paper, a method based on the ensemble empirical mode decomposition of permutation entropy is proposed to extract the nonlinear characteristic parameters of the vibration signal of the high speed train, and used to identify the fault state. Firstly, a series of narrow band intrinsic mode functions are obtained by the empirical mode decomposition of vibration signals. Then, the original signal and the instrinsic mode function are calculated respectively and several groups of permutation entropy are got, and a multi -scale high dimensional feature vector is formed. Finally, the high dimensional feature vector is input to the least squares support vector machine to classify the fault state of the axle bearing. The results of bench test showed that the method has high recognition rate for the axle bearing fault of high-speed train, especially axle bearing compound fault, and the effectives of the method on the high -speed train axle bearing fault diagnosis by ensemble empirical mode decomposition permutation entropy are verified.%高速列车轮对轴承的可靠度对高速列车的安全运行具有重要意义,其故障特征主要体现在轴箱振动信号中.该文提出基于聚合经验模态分解排列熵的轮对轴承特征分析方法,提取高速列车轮对轴承振动信号的非线性特征参数,并用于故障状态的分类识别.首先,对高速列车轮对轴箱振动信号进行聚合经验模态分解,得到一系列窄带本征模态函数;然后,对原信号和主要本征模态函数分别计算,得到多组排列熵,形成多尺度的表征信息复杂性高维特征向量;最后,将高维特征向量输入最小二乘支持向量机分类识别出轮对轴承的故障状态.台架试验分析结果表明:该方法针对高速列车轮对轴承故障尤其是轴承复合故障具有较高的识别率,验证通过聚合经验模态分解排列熵对高速列车轮对轴承故障诊断的有效性.