高速列车转向架故障信号的聚合经验模态分解和 模糊熵特征分析

为了对高速列车转向架关键部件进行状态监测,利用转向架故障振动信号的特点,提出了一种结合聚合经验模态分解和模糊熵的特征提取方法.对故障信号进行聚合经验模态分解,得到一系列具有不同物理意义的简单成分信号,采用相关分析法选取最能反映原信号特征的本征模态函数.对这些本征模态函数和原信号分别计算模糊熵值构成多尺度复杂性度量的特征向量,输入最小二乘支持向量机中进行分类识别,与模糊熵特征相比得到了更好的识别效果,证明了算法的有效性.

Ensemble empirical mode decomposition and fuzzy entropy in fault feature analysis for high-speed train bogie

To monitor the working condition of key components in a high-speed train bogie, we make use of the char- acteristics of the fault signal to propose a new approach for feature extraction based on ensemble empirical mode de- composition and fuzzy entropy. Firstly, we decompose the fault vibration signal by using the ensemble empirical mode decomposition to obtain a series of simple composition signal with different physical significance. Then, we employ the correlation analysis to sift out intrinsic mode functions (IMF) that have largest correlation coefficients with the original signal and use it as the data source.The fuzzy entropy of IMF and the fuzzy entropy of the initial signal are calculated to form a multi-scale complexity measure feature vector. Finally, the feature vector is put into the least squares support vector machine for classification and identification. Comparing with the fuzzy entropy, we find the proposed approach gives better fault identification results. The efficacy of this method is validated.