VMD多尺度熵用于高速列车横向减振器故障诊断

针对高速列车横向减振器故障振动信号具有非线性和非平稳特征、特征信号提取相对困难问题,提出了变分模态分解和多尺度熵结合的特征提取方法。原始信号经变分模态分解方法处理后,被分解为若干本征模态,利用互信息指标筛选有效模态,求多尺度熵组成特征向量,通过特征评价方法去除冗余特征,最终将最优特征子集输入支持向量机识别横向减振器的故障类型。实验结果表明,该方法能有效提取振动信号的特征,实现横向减振器故障的有效判别,验证了该方法在高速列车横向减振器故障诊断的可行性。

Fault Diagnosis Method for High Speed Train Lateral Damper Based on Variational Mode Decomposition and Multiscale Entropy

In the fault period of high-speed train lateral damper, the vibration signal is non-linear and nonstationary, and features extracting is relatively difficult. In order to solve this problem, a method of features extracting based on variational mode decompositi on and multiscale entropy is proposed. The original signal is decomposed into several intrinsic mode function components after being processed by the variational mode decomposition method. Then, the best component is selected by the mutual information index. The feature matrix is constructed through the multiscale entropy of the best component, and removed redundant features using feature evaluation algorithm. The fault type of lateral damper is judged by transforming in the best subset of feature matrix in support vector machine. Experimental results show that the proposed method can extract the feature and judge the fault type of lateral damper effectively, which proves the feasibility of this mechanical fault diagnosis method.